Last update:
Fri Oct 4 06:45:39 MDT 2024
Stephen E. Fienberg When did Bayesian inference become
``Bayesian''? . . . . . . . . . . . . . 1--40
Alan E. Gelfand and
John A. Silander, Jr. and
Shanshan Wu and
Andrew Latimer and
Paul O. Lewis and
Anthony G. Rebelo and
Mark Holder Explaining species distribution patterns
through hierarchical modeling . . . . . 41--92
Jennifer A. Hoeting Some perspectives on modeling species
distributions (comment on article by
Gelfand et al.) . . . . . . . . . . . . 93--97
Jay M. Ver Hoef Comment on article by Gelfand et al. . . 99--101
Alan E. Gelfand and
John A. Silander, Jr. and
Shanshan Wu and
Andrew Latimer and
Paul O. Lewis and
Anthony G. Rebelo and
Mark Holder Rejoinder . . . . . . . . . . . . . . . 103--104
Leanna L. House and
Merlise A. Clyde and
Yuh-Chin T. Huang Bayesian Identification of Differential
Gene Expression Induced by Metals in
Human Bronchial Epithelial Cells . . . . 105--120
David M. Blei and
Michael I. Jordan Variational inference for Dirichlet
process mixtures . . . . . . . . . . . . 121--143
Chris C. Holmes and
Leonhard Held Bayesian auxiliary variable models for
binary and multinomial regression . . . 145--168
J. A. A. Andrade and
A. O'Hagan Bayesian robustness modeling using
regularly varying distributions . . . . 169--188
Anonymous Table of Contents . . . . . . . . . . . ??
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David A. van Dyk and
Alanna Connors and
David N. Esch and
Peter Freeman and
Hosung Kang and
Margarita Karovska and
Vinay Kashyap and
Aneta Siemiginowska and
Andreas Zezas Deconvolution in High-Energy
Astrophysics: Science, Instrumentation,
and Methods . . . . . . . . . . . . . . 189--235
Ji Meng Loh and
Andrew Gelman Comment on article by van Dyk et al. . . 237--240
David A. van Dyk and
Hosung Kang Rejoinder . . . . . . . . . . . . . . . 241--248
Herbert K. H. Lee and
Bruno Sansó and
Weining Zhou and
David M. Higdon Inferring Particle Distribution in a
Proton Accelerator Experiment . . . . . 249--264
Caitlin E. Buck and
Delil Gómez Portugal Aguilar and
Cliff D. Litton and
Anthony O'Hagan Bayesian nonparametric estimation of the
radiocarbon calibration curve . . . . . 265--288
Edoardo M. Airoldi and
Annelise G. Anderson and
Stephen E. Fienberg and
Kiron K. Skinner Who wrote Ronald Reagan's radio
addresses? . . . . . . . . . . . . . . . 289--319
Peter D. Hoff Model-based subspace clustering . . . . 321--344
Suhrid Balakrishnan and
David Madigan A one-pass sequential Monte Carlo method
for Bayesian analysis of massive
datasets . . . . . . . . . . . . . . . . 345--361
Joseph B. Kadane and
Galit Shmueli and
Thomas P. Minka and
Sharad Borle and
Peter Boatwright Conjugate Analysis of the
Conway--Maxwell--Poisson Distribution 363--374
Christopher J. Paciorek Misinformation in the conjugate prior
for the linear model with implications
for free-knot spline modelling . . . . . 375--383
Anonymous Table of Contents . . . . . . . . . . . ??
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James Berger The Case for Objective Bayesian Analysis 385--402
Michael Goldstein Subjective Bayesian Analysis: Principles
and Practice . . . . . . . . . . . . . . 403--420
J. Andrés Christen Stop using `subjective' to refer to
Bayesian analyses (comment on articles
by Berger and by Goldstein) . . . . . . 421--422
David Draper Coherence and calibration: comments on
subjectivity and ``objectivity'' in
Bayesian analysis (comment on articles
by Berger and by Goldstein) . . . . . . 423--428
Stephen E. Fienberg Does it make sense to be an ``objective
Bayesian''? (comment on articles by
Berger and by Goldstein) . . . . . . . . 429--432
Joseph B. Kadane Is ``objective Bayesian analysis''
objective, Bayesian, or wise? (comment
on articles by Berger and by Goldstein) 433--436
Robert E. Kass Kinds of Bayesians (comment on articles
by Berger and by Goldstein) . . . . . . 437--440
Frank Lad Objective Bayesian statistics \ldots. Do
you buy it? Should we sell it? (comment
on articles by Berger and by Goldstein) 441--444
Anthony O'Hagan Science, subjectivity and software
(comment on articles by Berger and by
Goldstein) . . . . . . . . . . . . . . . 445--450
Larry Wasserman Frequentist Bayes is objective (comment
on articles by Berger and by Goldstein) 451--456
James Berger Rejoinder . . . . . . . . . . . . . . . 457--464
Michael Goldstein Subjectivity and objectivity in Bayesian
statistics: rejoinder to the discussion 465--472
William J. Browne and
David Draper A comparison of Bayesian and
likelihood-based methods for fitting
multilevel models . . . . . . . . . . . 473--514
Andrew Gelman Prior distributions for variance
parameters in hierarchical models
(comment on article by Browne and
Draper) . . . . . . . . . . . . . . . . 515--534
Robert E. Kass and
Ranjini Natarajan A default conjugate prior for variance
components in generalized linear mixed
models (comment on article by Browne and
Draper) . . . . . . . . . . . . . . . . 535--542
Paul C. Lambert (Comment on Article by Browne and
Draper) . . . . . . . . . . . . . . . . 543--546
William J. Browne and
David Draper Rejoinder . . . . . . . . . . . . . . . 547--550
Ming-Hui Chen and
Joseph G. Ibrahim The Relationship Between the Power Prior
and Hierarchical Models . . . . . . . . 551--574
Timothy E. Hanson Modeling Censored Lifetime Data Using a
Mixture of Gammas Baseline . . . . . . . 575--594
Margaret B. Short and
Bradley P. Carlin Multivariate Spatiotemporal CDFs with
Random Effects and Measurement Error . . 595--624
Bo Wang and
D. M. Titterington Convergence properties of a general
algorithm for calculating variational
Bayesian estimates for a normal mixture
model . . . . . . . . . . . . . . . . . 625--650
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G. Celeux and
F. Forbes and
C. P. Robert and
D. M. Titterington Deviance Information Criteria for
Missing Data Models . . . . . . . . . . 651--673
Bradley P. Carlin Comment on article by Celeux et al. . . 675--676
Ming-Hui Chen Comments on article by Celeux et al. . . 677--680
Martyn Plummer Comment on article by Celeux et al. . . 681--686
Xiao-Li Meng and
Florin Vaida Comment on article by Celeux et al. . . 687--698
Angelika van der Linde Comment on article by Celeux et al. . . 699--700
G. Celeux and
F. Forbes and
C. P. Robert and
D. M. Titterington Rejoinder . . . . . . . . . . . . . . . 701--705
Paola Sebastiani and
Hui Xie and
Marco F. Ramoni Bayesian Analysis of Comparative
Microarray Experiments by Model
Averaging . . . . . . . . . . . . . . . 707--732
Fabio Rigat and
Mathisca de Gunst and
Jaap van Pelt Bayesian Modelling and Analysis of
Spatio-Temporal Neuronal Networks . . . 733--764
Brian Williams and
Dave Higdon and
Jim Gattiker and
Leslie Moore and
Michael McKay and
Sallie Keller-McNulty Combining Experimental Data and Computer
Simulations, With an Application to
Flyer Plate Experiments . . . . . . . . 765--792
Matthew J. Beal and
Zoubin Ghahramani Variational Bayesian Learning of
Directed Graphical Models with Hidden
Variables . . . . . . . . . . . . . . . 793--831
John Skilling Nested Sampling for General Bayesian
Computation . . . . . . . . . . . . . . 833--859
Jorge L. Bazán and
Marcia D. Branco and
Heleno Bolfarine A Skew Item Response Model . . . . . . . 861--892
Michael Evans and
Hadas Moshonov Checking for Prior-Data Conflict . . . . 893--914
Rongheng Lin and
Thomas A. Louis and
Susan M. Paddock and
Greg Ridgeway Loss Function Based Ranking in
Two-Stage, Hierarchical Models . . . . . 915--946
Marco A. R. Ferreira and
Mike West and
Herbert K. H. Lee and
David M. Higdon Multi-Scale and Hidden Resolution Time
Series Models . . . . . . . . . . . . . 947--967
Dale J. Poirier The Growth of Bayesian Methods in
Statistics and Economics Since 1970 . . 969--979
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Francesca Dominici and
Scott L. Zeger and
Giovanni Parmigiani and
Joanne Katz and
Parul Christian Does the effect of micronutrient
supplementation on neonatal survival
vary with respect to the percentiles of
the birth weight distribution? . . . . . 1--30
Samantha R. Cook and
Elizabeth A. Stuart Comment on article by Dominici et al. 31--35
David Ruppert and
Raymond J. Carroll Comment on article by Dominici et al. 37--42
Francesca Dominici and
Scott L. Zeger and
Giovanni Parmigiani and
Joanne Katz and
Parul Christian Rejoinder . . . . . . . . . . . . . . . 43--44
José M. Bernardo and
Sergio Pérez Comparing Normal Means: New Methods for
an Old Problem . . . . . . . . . . . . . 45--58
Juan Antonio Cano and
Mathieu Kessler and
Diego Salmerón Integral priors for the one way random
effects model . . . . . . . . . . . . . 59--67
Carlos M. Carvalho and
Mike West Dynamic Matrix-Variate Graphical Models 69--97
Robert Denham and
Kerrie Mengersen Geographically Assisted Elicitation of
Expert Opinion for Regression Models . . 99--135
Dipak K. Dey and
Junfeng Liu A Quantitative Study of Quantile Based
Direct Prior Elicitation from Expert
Opinion . . . . . . . . . . . . . . . . 137--166
Josep Ginebra On the Measure of the Information in a
Statistical Experiment . . . . . . . . . 167--211
George Kokolakis and
George Kouvaras On the Multimodality of Random
Probability Measures . . . . . . . . . . 213--219
Babak Shahbaba and
Radford M. Neal Improving Classification When a Class
Hierarchy is Available Using a
Hierarchy-Based Prior . . . . . . . . . 221--237
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Kert Viele Nonparametric Estimation of
Kullback--Leibler Information
Illustrated by Evaluating Goodness of
Fit . . . . . . . . . . . . . . . . . . 239--280
Haijun Ma and
Bradley P. Carlin Bayesian Multivariate Areal Wombling for
Multiple Disease Boundary Analysis . . . 281--302
Carlos Almeida and
Michel Mouchart Bayesian encompassing specification test
under not completely known partial
observability . . . . . . . . . . . . . 303--318
Angelika van der Linde Local Influence on Posterior
Distributions under Multiplicative Modes
of Perturbation . . . . . . . . . . . . 319--332
R. G. Cowell and
S. L. Lauritzen and
J. Mortera A gamma model for DNA mixture analyses 333--348
Josemar Rodrigues and
Heleno Bolfarine Bayesian inference for an extended
simple regression measurement error
model using skewed priors . . . . . . . 349--364
Russell B. Millar and
Wayne S. Stewart Assessment of Locally Influential
Observations in Bayesian Models . . . . 365--383
S. Bhattacharya and
J. Haslett Importance Re-sampling MCMC for
Cross-Validation in Inverse Problems . . 385--407
E. C. Marshall and
D. J. Spiegelhalter Identifying outliers in Bayesian
hierarchical models: a simulation-based
approach . . . . . . . . . . . . . . . . 409--444
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Sonia Jain and
Radford M. Neal Splitting and Merging Components of a
Nonconjugate Dirichlet Process Mixture
Model . . . . . . . . . . . . . . . . . 445--472
David B. Dahl Comment on article by Jain and Neal . . 473--477
C. P. Robert Comment on article by Jain and Neal . . 479--482
Steven N. MacEachern Comment on article by Jain and Neal . . 483--494
Sonia Jain and
Radford M. Neal Rejoinder . . . . . . . . . . . . . . . 495--500
Eric P. Xing and
Kyung-Ah Sohn Hidden Markov Dirichlet Process:
Modeling Genetic Inference in Open
Ancestral Space . . . . . . . . . . . . 501--527
Yi He and
James S. Hodges and
Bradley P. Carlin Re-considering the variance
parameterization in multiple precision
models . . . . . . . . . . . . . . . . . 529--556
Ana Maria Madrigal Cluster Allocation Design Networks . . . 557--589
Vanja Duki\'c and
James Dignam Bayesian Hierarchical Multiresolution
Hazard Model for the Study of
Time-Dependent Failure Patterns in Early
Stage Breast Cancer . . . . . . . . . . 591--609
Song Zhang and
Ya-Chen Tina Shih and
Peter Müller A Spatially-adjusted Bayesian Additive
Regression Tree Model to Merge Two
Datasets . . . . . . . . . . . . . . . . 611--633
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Marcus Hutter Exact Bayesian Regression of Piecewise
Constant Functions . . . . . . . . . . . 635--664
Margaret B. Short and
David M. Higdon and
Philipp P. Kronberg Estimation of Faraday Rotation Measures
of the Near Galactic Sky Using Gaussian
Process Models . . . . . . . . . . . . . 665--680
Pierre Druilhet and
Jean-Michel Marin Invariant HPD credible sets and MAP
estimators . . . . . . . . . . . . . . . 681--691
John Paul Gosling and
Jeremy E. Oakley and
Anthony O'Hagan Nonparametric elicitation for
heavy-tailed prior distributions . . . . 693--718
Valen E. Johnson Bayesian Model Assessment Using Pivotal
Quantities . . . . . . . . . . . . . . . 719--733
Guofen Yan and
J. Sedransk Bayesian Diagnostic Techniques for
Detecting Hierarchical Structure . . . . 735--760
Jesper Mòller and
Kerrie Mengersen Ergodic averages for monotone functions
using upper and lower dominating
processes . . . . . . . . . . . . . . . 761--781
Sourabh Bhattacharya A Simulation Approach to Bayesian
Emulation of Complex Dynamic Computer
Models . . . . . . . . . . . . . . . . . 783--815
Mario Peruggia Bayesian Model Diagnostics Based on
Artificial Autoregressive Errors . . . . 817--841
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Bruno Sansó and
Chris E. Forest and
Daniel Zantedeschi Inferring Climate System Properties
Using a Computer Model . . . . . . . . . 1--37
Dave Higdon and
James Gattiker Comment on article by Sansó et al.
[MR2383247] . . . . . . . . . . . . . . 39--44
Jonathan Rougier Comment on article by Sansó et al.
[MR2383247] . . . . . . . . . . . . . . 45--56
Bruno Sansó and
Chris E. Forest and
Daniel Zantedeschi Rejoinder . . . . . . . . . . . . . . . 57--61
Ivan Jeliazkov and
Dale J. Poirier Dynamic and structural features of
intifada violence: a Markov process
approach . . . . . . . . . . . . . . . . 63--77
Carlos A. de B. Pereira and
Julio Michael Stern and
Sergio Wechsler Can a significance test be genuinely
Bayesian? . . . . . . . . . . . . . . . 79--100
Peter McCullagh and
Jie Yang How many clusters? . . . . . . . . . . . 101--120
Dan J. Spitzner An asymptotic viewpoint on
high-dimensional Bayesian testing . . . 121--160
John Aldrich R. A. Fisher on Bayes and Bayes' theorem 161--170
Longhai Li and
Jianguo Zhang and
Radford M. Neal A method for avoiding bias from feature
selection with application to naive
Bayes classification models . . . . . . 171--196
Sonali Das and
Ming-Hui Chen and
Sungduk Kim and
Nicholas Warren A Bayesian Structural Equations Model
for Multilevel Data with Missing
Responses and Missing Covariates . . . . 197--224
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P. G. Blackwell and
C. E. Buck Estimating radiocarbon calibration
curves . . . . . . . . . . . . . . . . . 225--248
John Haslett and
Andrew Parnell Comment on article by Blackwell and Buck 249--254
Andrew R. Millard Comment on article by Blackwell and Buck 255--261
P. G. Blackwell and
C. E. Buck Rejoinder . . . . . . . . . . . . . . . 263--268
Cyr E. M'Lan and
Lawrence Joseph and
David B. Wolfson Bayesian Sample Size Determination for
Binomial Proportions . . . . . . . . . . 269--296
José T. A. S. Ferreira and
Miguel A. Juárez and
Mark F. J. Steel Directional log-spline distributions . . 297--316
Fernando A. Quintana and
Peter Müller and
Gary L. Rosner and
Mark Munsell Semi-parametric Bayesian Inference for
Multi-Season Baseball Data . . . . . . . 317--338
Abel Rodriguez and
Enrique ter Horst Bayesian dynamic density estimation . . 339--365
Jessica Tressou Bayesian nonparametrics for heavy tailed
distribution. Application to food risk
assessment . . . . . . . . . . . . . . . 367--391
Ivilina Popova and
Elmira Popova and
Edward I. George Bayesian Forecasting of Prepayment Rates
for Individual Pools of Mortgages . . . 393--426
Christian P. Robert and
Jean-Michel Marin On some difficulties with a posterior
probability approximation technique . . 427--441
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Bradley P. Carlin Editor in Chief's note . . . . . . . . . 443--444
Andrew Gelman Objections to Bayesian statistics . . . 445--449
José M. Bernardo Comment on article by Gelman . . . . . . 451--453
Joseph B. Kadane Comment on article by Gelman . . . . . . 455--457
Stephen Senn Comment on article by Gelman . . . . . . 459--461
Larry Wasserman Comment on article by Gelman . . . . . . 463--465
Andrew Gelman Rejoinder . . . . . . . . . . . . . . . 467--477
Sam K. Hui and
Yanliu Huang and
Edward I. George Model-based Analysis of Concept Maps . . 479--512
Reinaldo B. Arellano-Valle and
Luis M. Castro and
Marc G. Genton and
Héctor W. Gómez Bayesian inference for shape mixtures of
skewed distributions, with application
to regression analysis . . . . . . . . . 513--539
Thomas J. Jiang and
James M. Dickey Bayesian methods for categorical data
under informative censoring . . . . . . 541--553
Simo Särkkä and
Tommi Sottinen Application of Girsanov Theorem to
Particle Filtering of Discretely
Observed Continuous-Time Non-Linear
Systems . . . . . . . . . . . . . . . . 555--584
Ming-Hui Chen and
Lan Huang and
Joseph G. Ibrahim and
Sungduk Kim Bayesian variable selection and
computation for generalized linear
models with conjugate priors . . . . . . 585--613
Peter M. Hooper Exact distribution theory for belief net
responses . . . . . . . . . . . . . . . 615--624
Jarrett J. Barber and
Steven D. Prager Combining multiple maps of line features
to infer true position . . . . . . . . . 625--658
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Tobias Rydén EM versus Markov chain Monte Carlo for
estimation of hidden Markov models: a
computational perspective . . . . . . . 659--688
Sylvia Frühwirth-Schnatter Comment on article by Rydén . . . . . . . 689--697
Padhraic Smyth and
Sergey Kirshner Comment on article by Rydén . . . . . . . 699--705
Tobias Rydén Rejoinder . . . . . . . . . . . . . . . 707--715
V. E. Rapley and
A. H. Welsh Model-based inferences from adaptive
cluster sampling . . . . . . . . . . . . 717--736
Damian Clancy and
Philip D. O'Neill Bayesian estimation of the basic
reproduction number in stochastic
epidemic models . . . . . . . . . . . . 737--757
Hedibert Freitas Lopes and
Esther Salazar and
Dani Gamerman Spatial Dynamic Factor Analysis . . . . 759--792
Longhai Li and
Radford M. Neal Compressing parameters in Bayesian
high-order models with application to
logistic sequence models . . . . . . . . 793--821
Alejandro Villagran and
Gabriel Huerta and
Charles S. Jackson and
Mrinal K. Sen Computational Methods for Parameter
Estimation in Climate Models . . . . . . 823--850
Vilda Purutçuo\uglu and
Ernst Wit Bayesian inference for the MAPK/ERK
pathway by considering the dependency of
the kinetic parameters . . . . . . . . . 851--886
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Peter F. Craigmile and
Catherine A. Calder and
Hongfei Li and
Rajib Paul and
Noel Cressie Hierarchical Model Building, Fitting,
and Checking: A Behind-the-Scenes Look
at a Bayesian Analysis of Arsenic
Exposure Pathways . . . . . . . . . . . 1--35
Christopher David Barr and
Francesca Dominici Comment on article by Craigmile et al. 37--39
David B. Dunson Comment on article by Craigmile et al. 41--43
Alexandra M. Schmidt Comment on article by Craigmile et al. 45--53
Peter F. Craigmile and
Catherine A. Calder and
Hongfei Li and
Rajib Paul and
Noel Cressie Rejoinder . . . . . . . . . . . . . . . 55--62
Markus Hahn and
Jörn Sassy Parameter estimation in continuous time
Markov switching models: a
semi-continuous Markov chain Monte Carlo
approach . . . . . . . . . . . . . . . . 63--84
R. B. O'Hara and
M. J. Sillanpää A review of Bayesian variable selection
methods: what, how and which . . . . . . 85--117
F. Liu and
M. J. Bayarri and
J. O. Berger Modularization in Bayesian Analysis,
with Emphasis on Analysis of Computer
Models . . . . . . . . . . . . . . . . . 119--150
Frank Tuyl and
Richard Gerlach and
Kerrie Mengersen Posterior predictive arguments in favor
of the Bayes--Laplace prior as the
consensus prior for binomial and
multinomial parameters . . . . . . . . . 151--158
Scott Holan and
Tucker McElroy and
Sounak Chakraborty A Bayesian Approach to Estimating the
Long Memory Parameter . . . . . . . . . 159--190
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Guosheng Yin Bayesian generalized method of moments 191--207
Ming-Hui Chen and
Sungduk Kim Comments on article by Yin . . . . . . . 209--212
Ciprian M. Crainiceanu Comments on article by Yin . . . . . . . 213--215
Guosheng Yin Rejoinder . . . . . . . . . . . . . . . 217--222
E. Gómez-Déniz Some Bayesian credibility premiums
obtained by using posterior regret $
\Gamma $-minimax methodology . . . . . . 223--242
David B. Dahl Modal clustering in a class of product
partition models . . . . . . . . . . . . 243--264
Isobel Claire Gormley and
Thomas Brendan Murphy A grade of membership model for rank
data . . . . . . . . . . . . . . . . . . 265--295
Chunlin Ji and
Daniel Merl and
Thomas B. Kepler and
Mike West Spatial mixture modelling for unobserved
point processes: examples in
immunofluorescence histology . . . . . . 297--315
Aude Grelaud and
Christian P. Robert and
Jean-Michel Marin and
François Rodolphe and
Jean-François Taly ABC likelihood-free methods for model
choice in Gibbs random fields . . . . . 317--335
James S. Clark and
Michelle H. Hersh Inference in incidence, infection, and
impact: Co-infection of multiple hosts
by multiple pathogens . . . . . . . . . 337--365
Arno Fritsch and
Katja Ickstadt Improved criteria for clustering based
on the posterior similarity matrix . . . 367--391
Fei Liu and
Mike West A dynamic modelling strategy for
Bayesian computer model emulation . . . 393--411
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Stefano Monni and
Mahlet G. Tadesse A stochastic partitioning method to
associate high-dimensional responses and
covariates . . . . . . . . . . . . . . . 413--436
Hugh Chipman and
Edward George and
Robert McCulloch Comment on article by Monni and Tadesse 437--438
Chris Fraley Comment on article by Monni and Tadesse 439--447
Hongzhe Li Comment on article by Monni and Tadesse 449--452
Hal Stern Comment on article by Monni and Tadesse 453--456
Stefano Monni and
Mahlet G. Tadesse Rejoinder . . . . . . . . . . . . . . . 457--464
Chris P. Jewell and
Theodore Kypraios and
Peter Neal and
Gareth O. Roberts Bayesian analysis for emerging
infectious diseases . . . . . . . . . . 465--496
Fernando A. Quintana and
Mark F. J. Steel and
José T. A. S. Ferreira Flexible Univariate Continuous
Distributions . . . . . . . . . . . . . 497--521
Julie Horrocks and
Marianne J. van Den Heuvel Prediction of pregnancy: a joint model
for longitudinal and binary data . . . . 523--538
Silvia Liverani and
Paul E. Anderson and
Kieron D. Edwards and
Andrew J. Millar and
Jim Q. Smith Efficient Utility-based Clustering over
High Dimensional Partition Spaces . . . 539--571
David S. Leslie and
Robert Kohn and
Denzil G. Fiebig Nonparametric estimation of the
distribution function in contingent
valuation models . . . . . . . . . . . . 573--597
Maurice J. Dupré and
Frank J. Tipler New axioms for rigorous Bayesian
probability . . . . . . . . . . . . . . 599--606
Melissa A. Bingham and
Stephen B. Vardeman and
Daniel J. Nordman Bayes one-sample and one-way random
effects analyses for $3$-D orientations
with application to materials science 607--629
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Shane T. Jensen and
Blakeley B. McShane and
Abraham J. Wyner Hierarchical Bayesian modeling of
hitting performance in baseball . . . . 631--652
Jim Albert and
Phil Birnbaum Comment on article by Jensen et al. . . 653--660
Mark E. Glickman Comment on article by Jensen et al. . . 661--664
Fernando A. Quintana and
Peter Müller Comment on article by Jensen et al. . . 665--668
Shane T. Jensen and
Blakeley B. McShane and
Abraham J. Wyner Rejoinder . . . . . . . . . . . . . . . 669--674
Minjung Kyung and
Sujit K. Ghosh Bayesian Inference for Directional
Conditionally Autoregressive Models . . 675--706
Sinae Kim and
David B. Dahl and
Marina Vannucci Spiked Dirichlet Process Prior for
Bayesian Multiple Hypothesis Testing in
Random Effects Models . . . . . . . . . 707--732
Jason A. Duan and
Alan E. Gelfand and
C. F. Sirmans Modeling space-time data using
stochastic differential equations . . . 733--758
Ronald Christensen Inconsistent Bayesian estimation . . . . 759--762
Hongmei Zhang and
Hal Stern Sample Size Calculation for Finding
Unseen Species . . . . . . . . . . . . . 763--792
Matthew A. Taddy and
Athanasios Kottas Markov Switching Dirichlet Process
Mixture Regression . . . . . . . . . . . 793--816
Jairo A. Fúquene and
John D. Cook and
Luis R. Pericchi A Case for Robust Bayesian Priors with
Applications to Clinical Trials . . . . 817--846
Bradley P. Carlin Editor-in-chief's note . . . . . . . . . 847--850
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Charles R. Hogg and
Joseph B. Kadane and
Jong Soo Lee and
Sara A. Majetich Error analysis for small angle neutron
scattering datasets using Bayesian
inference . . . . . . . . . . . . . . . 1--33
Nick Hengartner Comment on article by Hogg et al. . . . 35--37
John Skilling and
Devinder Sivia Comment on article by Hogg et al. . . . 39--40
Charles R. Hogg and
Joseph B. Kadane and
Jong Soo Lee and
Sara A. Majetich Rejoinder . . . . . . . . . . . . . . . 41--43
J. E. Griffin Default priors for density estimation
with mixture models . . . . . . . . . . 45--64
Tomohiro Ando and
Arnold Zellner Hierarchical Bayesian analysis of the
seemingly unrelated regression and
simultaneous equations models using a
combination of direct Monte Carlo and
importance sampling techniques . . . . . 65--95
Avishek Chakraborty and
Alan E. Gelfand Analyzing spatial point patterns subject
to measurement error . . . . . . . . . . 97--122
Cari G. Kaufman and
Stephan R. Sain Bayesian functional ANOVA modeling using
Gaussian process prior distributions . . 123--149
Qing Li and
Nan Lin The Bayesian elastic net . . . . . . . . 151--170
Jim E. Griffin and
Philip J. Brown Inference with normal-gamma prior
distributions in regression problems . . 171--188
Xiaoxi Zhang and
Timothy D. Johnson and
Roderick J. A. Little and
Yue Cao A Bayesian Image Analysis of Radiation
Induced Changes in Tumor Vascular
Permeability . . . . . . . . . . . . . . 189--212
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Christian P. Robert The search for certainty: a critical
assessment . . . . . . . . . . . . . . . 213--222
Larry Wasserman Comment on Article by Robert . . . . . . 223--228
Andrew Gelman Comment on Article by Robert . . . . . . 229--232
Krzysztof Burdzy Comment on Article by Robert . . . . . . 233--236
Robert B. Gramacy and
Ester Pantaleo Shrinkage regression for multivariate
inference with missing data, and an
application to portfolio balancing . . . 237--262
J. Andrés Christen and
Colin Fox A general purpose sampling algorithm for
continuous distributions (the $t$-walk) 263--281
Bertrand Clarke Desiderata for a predictive theory of
statistics . . . . . . . . . . . . . . . 283--318
Surya T. Tokdar and
Yu M. Zhu and
Jayanta K. Ghosh Bayesian Density Regression with
Logistic Gaussian Process and Subspace
Projection . . . . . . . . . . . . . . . 319--344
Christoph Pamminger and
Sylvia Frühwirth-Schnatter Model-based Clustering of Categorical
Time Series . . . . . . . . . . . . . . 345--368
Minjung Kyung and
Jeff Gill and
Malay Ghosh and
George Casella Penalized Regression, Standard Errors,
and Bayesian Lassos . . . . . . . . . . 369--411
Jing Cao and
Song Zhang Measuring statistical significance for
full Bayesian methods in microarray
analyses . . . . . . . . . . . . . . . . 413--427
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Ioanna Manolopoulou and
Cliburn Chan and
Mike West Selection Sampling from Large Data Sets
for Targeted Inference in Mixture
Modeling . . . . . . . . . . . . . . . . 429--449
Fabio Rigat Comment on article by Manolopoulou et
al. . . . . . . . . . . . . . . . . . . 451--455
Nick Whiteley Comment on article by Manolopoulou et
al. . . . . . . . . . . . . . . . . . . 457--460
Ioanna Manolopoulou and
Cliburn Chan and
Mike West Rejoinder . . . . . . . . . . . . . . . 461--463
Gareth W. Peters and
Balakrishnan Kannan and
Ben Lasscock and
Chris Mellen Model Selection and Adaptive Markov
chain Monte Carlo for Bayesian
Cointegrated VAR Models . . . . . . . . 465--491
Anandamayee Majumdar and
Debashis Paul and
Jason Kaye Sensitivity analysis and model selection
for a generalized convolution model for
spatial processes . . . . . . . . . . . 493--518
Jules J. S. de Tibeiro and
Duncan J. Murdoch Correspondence Analysis with Incomplete
Paired Data using Bayesian Imputation 519--532
Qing Li and
Ruibin Xi and
Nan Lin Bayesian regularized quantile regression 533--556
Margaret Short and
Dave Higdon and
Laura Guadagnini and
Alberto Guadagnini and
Daniel M. Tartakovsky Predicting Vertical Connectivity Within
an Aquifer System . . . . . . . . . . . 557--581
Leonard Bottolo and
Sylvia Richardson Evolutionary stochastic search for
Bayesian model exploration . . . . . . . 583--618
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Ian Vernon and
Michael Goldstein and
Richard G. Bower Galaxy Formation: a Bayesian Uncertainty
Analysis . . . . . . . . . . . . . . . . 619--669
David Poole Comment on article by Vernon et al. . . 671--675
Pritam Ranjan Comment on article by Vernon et al. . . 677--681
Earl Lawrence and
David M. Higdon Comment on Article by Vernon et al. . . 683--689
David A. van Dyk Comment on article by Vernon et al. . . 691--695
Ian Vernon and
Michael Goldstein and
Richard G. Bower Rejoinder . . . . . . . . . . . . . . . 697--708
Carlos M. Carvalho and
Hedibert F. Lopes and
Nicholas G. Polson and
Matt A. Taddy Particle learning for general mixtures 709--740
Ruby C. Weng A Bayesian Edgeworth expansion by
Stein's identity . . . . . . . . . . . . 741--763
Kathryn Barger and
John Bunge Objective Bayesian estimation for the
number of species . . . . . . . . . . . 765--785
S. A. Kharroubi and
T. J. Sweeting Posterior simulation via the signed root
log-likelihood ratio . . . . . . . . . . 787--815
XuanLong Nguyen Inference of global clusters from
locally distributed data . . . . . . . . 817--845
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Nicholas G. Polson and
Steven L. Scott Data augmentation for support vector
machines . . . . . . . . . . . . . . . . 1--23
Bani K. Mallick and
Sounak Chakraborty and
Malay Ghosh Comment on article by Polson and Scott 25--29
Babak Shahbaba and
Yaming Yu and
David A. van Dyk Comment on article by Polson and Scott 31--35
Chris Hans Comment on article by Polson and Scott 37--41
Nicholas G. Polson and
Steven L. Scott Rejoinder: ``Data augmentation for
support vector machines'' . . . . . . . 43--47
Xavier Didelot and
Richard G. Everitt and
Adam M. Johansen and
Daniel J. Lawson Likelihood-free estimation of model
evidence . . . . . . . . . . . . . . . . 49--76
Angelika van der Linde Reduced rank regression models with
latent variables in Bayesian functional
data analysis . . . . . . . . . . . . . 77--126
Amélie Crépet and
Jessica Tressou Bayesian nonparametric model for
clustering individual co-exposure to
pesticides found in the French diet . . 127--144
Abel Rodríguez and
David B. Dunson Nonparametric Bayesian models through
probit stick-breaking processes . . . . 145--177
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Peter D. Hoff Separable covariance arrays via the
Tucker product, with applications to
multivariate relational data . . . . . . 179--196
Genevera I. Allen Comment on article by Hoff . . . . . . . 197--201
Hedibert Freitas Lopes Comment on article by Hoff . . . . . . . 203--204
Peter D. Hoff Rejoinder: ``Comment on article by
Hoff'' . . . . . . . . . . . . . . . . . 205--207
Meli Baragatti Bayesian variable selection for probit
mixed models applied to gene selection 209--229
Osnat Stramer and
Matthew Bognar Bayesian inference for irreducible
diffusion processes using the
pseudo-marginal approach . . . . . . . . 231--258
Ma\lgorzata Roos and
Leonhard Held Sensitivity analysis in Bayesian
generalized linear mixed models for
binary data . . . . . . . . . . . . . . 259--278
Guy Freeman and
Jim Q. Smith Dynamic staged trees for discrete
multivariate time series: forecasting,
model selection and causal analysis . . 279--305
James G. Scott Bayesian estimation of intensity
surfaces on the sphere via needlet
shrinkage and selection . . . . . . . . 307--327
Christopher Yau and
Chris Holmes Hierarchical Bayesian nonparametric
mixture models for clustering with
variable relevance determination . . . . 329--351
Ralf van der Lans Bayesian estimation of the multinomial
logit model: a comment on Holmes and
Held, ``Bayesian auxiliary variable
models for binary and multinomial
regression'' . . . . . . . . . . . . . . 353--355
Chris Holmes and
Leonhard Held Response to van der Lans . . . . . . . . 357--358
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Sara Wade and
Silvia Mongelluzzo and
Sonia Petrone An enriched conjugate prior for Bayesian
nonparametric inference . . . . . . . . 359--385
Daniel Sabanés Bové and
Leonhard Held Hyper-$g$ Priors for Generalized Linear
Models . . . . . . . . . . . . . . . . . 387--410
Laura Ventura and
Walter Racugno Recent advances on Bayesian inference
for $ P(X < Y) $ . . . . . . . . . . . . 411--428
Stefano Cabras and
María Eugenia Castellanos and
Alicia Quirós Goodness-of-fit of conditional
regression models for multiple
imputation . . . . . . . . . . . . . . . 429--455
Maarten Blaauw and
J. Andrés Christen Flexible paleoclimate age-depth models
using an autoregressive gamma process 457--474
Eric B. Ford and
Althea V. Moorhead and
Dimitri Veras A Bayesian surrogate model for rapid
time series analysis and application to
exoplanet observations . . . . . . . . . 475--499
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Jason Wyse and
Nial Friel and
Håvard Rue Approximate simulation-free Bayesian
inference for multiple changepoint
models with dependence within segments 501--528
Paul Fearnhead Comment on Article by Wyse et al. . . . 529--532
Gary Koop Comment on Article by Wyse et al. . . . 533--540
Jason Wyse and
Nial Friel and
Håvard Rue Rejoinder: ``Comment on Article by Wyse
et al.'' . . . . . . . . . . . . . . . . 541--546
Surya T. Tokdar and
Iris Grossmann and
Joseph B. Kadane and
Anne-Sophie Charest and
Mitchell J. Small Impact of Beliefs About Atlantic
Tropical Cyclone Detection on
Conclusions About Trends in Tropical
Cyclone Numbers . . . . . . . . . . . . 547--572
Cinzia Viroli Model based clustering for three-way
data structures . . . . . . . . . . . . 573--602
Dan J. Spitzner Neutral-data comparisons for Bayesian
testing . . . . . . . . . . . . . . . . 603--638
Hao Wang and
Craig Reeson and
Carlos M. Carvalho Dynamic Financial Index Models: Modeling
Conditional Dependencies via Graphs . . 639--664
Matthew S. Shotwell and
Elizabeth H. Slate Bayesian Outlier Detection with
Dirichlet Process Mixtures . . . . . . . 665--690
João V. D. Monteiro and
Renato M. Assunção and
Rosangela H. Loschi Product partition models with correlated
parameters . . . . . . . . . . . . . . . 691--726
David Leonard Estimating a bivariate linear
relationship . . . . . . . . . . . . . . 727--754
Gareth W. Peters and
Balakrishnan Kannan and
Ben Lasscock and
Chris Mellen and
Simon Godsill Bayesian Cointegrated Vector
Autoregression Models Incorporating
alpha-stable Noise for Inter-day Price
Movements Via Approximate Bayesian
Computation . . . . . . . . . . . . . . 755--792
Minjung Kyung A Computational Bayesian Method for
Estimating the Number of Knots In
Regression Splines . . . . . . . . . . . 793--828
Luke Bornn and
François Caron Bayesian clustering in decomposable
graphs . . . . . . . . . . . . . . . . . 829--846
Matthew P. Wand and
John T. Ormerod and
Simone A. Padoan and
Rudolf Frührwirth Mean Field Variational Bayes for
Elaborate Distributions . . . . . . . . 847--900
Eleni-Ioanna Delatola and
Jim E. Griffin Bayesian Nonparametric Modelling of the
Return Distribution with Stochastic
Volatility . . . . . . . . . . . . . . . 901--926
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Alessio Sancetta Universality of Bayesian Predictions . . 1--36
Bertrand Clarke Comment on Article by Sancetta . . . . . 37--44
Feng Liang Comment on Article by Sancetta . . . . . 45--46
Alessio Sancetta Rejoinder . . . . . . . . . . . . . . . 47--50
Surya T. Tokdar and
Joseph B. Kadane Simultaneous Linear Quantile Regression:
A Semiparametric Bayesian Approach . . . 51--72
Peter Carbonetto and
Matthew Stephens Scalable Variational Inference for
Bayesian Variable Selection in
Regression, and Its Accuracy in Genetic
Association Studies . . . . . . . . . . 73--108
Alexina Mason and
Sylvia Richardson and
Nicky Best Two-Pronged Strategy for Using DIC to
Compare Selection Models with
Non-Ignorable Missing Responses . . . . 109--146
Timothy E. Hanson and
Alejandro Jara and
Luping Zhao A Bayesian Semiparametric
Temporally-Stratified Proportional
Hazards Model with Spatial Frailties . . 147--188
Yangxin Huang and
Getachew A. Dagne Simultaneous Bayesian Inference for
Skew-Normal Semiparametric Nonlinear
Mixed-Effects Models with Covariate
Measurement Errors . . . . . . . . . . . 189--210
Camila C. S. Caiado and
Richard W. Hobbs and
Michael Goldstein Bayesian Strategies to Assess
Uncertainty in Velocity Models . . . . . 211--234
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Kristian Lum and
Alan E. Gelfand Spatial Quantile Multiple Regression
Using the Asymmetric Laplace Process . . 235--258
Rajarshi Guhaniyogi and
Sudipto Banerjee Comment on Article by Lum and Gelfand 259--262
Nan Lin and
Chao Chang Comment on Article by Lum and Gelfand 263--270
Marco A. R. Ferreira Comment on Article by Lum and Gelfand 271--272
Kristian Lum and
Alan E. Gelfand Rejoinder . . . . . . . . . . . . . . . 273--276
Andrés F. Barrientos and
Alejandro Jara and
Fernando A. Quintana On the Support of MacEachern's Dependent
Dirichlet Processes and Extensions . . . 277--310
Vincent Rivoirard and
Judith Rousseau Posterior Concentration Rates for
Infinite Dimensional Exponential
Families . . . . . . . . . . . . . . . . 311--334
Matthew A. Taddy and
Athanasios Kottas Mixture Modeling for Marked Poisson
Processes . . . . . . . . . . . . . . . 335--362
Serena Arima and
Gauri S. Datta and
Brunero Liseo Objective Bayesian Analysis of a
Measurement Error Small Area Model . . . 363--384
Roberto Casarin and
Luciana Dalla Valle and
Fabrizio Leisen Bayesian Model Selection for Beta
Autoregressive Processes . . . . . . . . 385--410
M. J. Rufo and
J. Martín and
C. J. Pérez Log-Linear Pool to Combine Prior
Distributions: A Suggestion for a
Calibration-Based Approach . . . . . . . 411--438
Tamara Broderick and
Michael I. Jordan and
Jim Pitman Beta Processes, Stick-Breaking, and
Power Laws . . . . . . . . . . . . . . . 439--476
Gilles Celeux and
Mohammed El Anbari and
Jean-Michel Marin and
Christian P. Robert Regularization in Regression: Comparing
Bayesian and Frequentist Methods in a
Poorly Informative Situation . . . . . . 477--502
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Isabelle Albert and
Sophie Donnet and
Chantal Guihenneuc-Jouyaux and
Samantha Low-Choy and
Kerrie Mengersen and
Judith Rousseau Combining Expert Opinions in Prior
Elicitation . . . . . . . . . . . . . . 503--532
Simon French Comment on Article by Albert et al. . . 533--536
John Paul Gosling Comment on Article by Albert et al. . . 537--540
Isabelle Albert and
Sophie Donnet and
Chantal Guihenneuc-Jouyaux and
Samantha Low-Choy and
Kerrie Mengersen and
Judith Rousseau Rejoinder . . . . . . . . . . . . . . . 541--546
Kim Kenobi and
Ian L. Dryden Bayesian Matching of Unlabeled Point
Sets Using Procrustes and Configuration
Models . . . . . . . . . . . . . . . . . 547--566
Robert B. Gramacy and
Nicholas G. Polson Simulation-based Regularized Logistic
Regression . . . . . . . . . . . . . . . 567--590
Satoshi Morita and
Peter F. Thall and
Peter Müller Prior Effective Sample Size in
Conditionally Independent Hierarchical
Models . . . . . . . . . . . . . . . . . 591--614
Irene Vrbik and
Rob Deardon and
Zeny Feng and
Abbie Gardner and
John Braun Using Individual-Level Models for
Infectious Disease Spread to Model
Spatio-Temporal Combustion Dynamics . . 615--638
Brian P. Hobbs and
Daniel J. Sargent and
Bradley P. Carlin Commensurate Priors for Incorporating
Historical Information in Clinical
Trials Using General and Generalized
Linear Models . . . . . . . . . . . . . 639--674
Sabyasachi Mukhopadhyay and
Sourabh Bhattacharya Perfect Simulation for Mixtures with
Known and Unknown Number of Components 675--714
Antti Solonen and
Pirkka Ollinaho and
Marko Laine and
Heikki Haario and
Johanna Tamminen and
Heikki Järvinen Efficient MCMC for Climate Model
Parameter Estimation: Parallel Adaptive
Chains and Early Rejection . . . . . . . 715--736
Martin D. Weinberg Computing the Bayes Factor from a Markov
Chain Monte Carlo Simulation of the
Posterior Distribution . . . . . . . . . 737--770
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Sungduk Kim and
Rajeshwari Sundaram and
Germaine M. Buck Louis and
Cecilia Pyper Flexible Bayesian Human Fecundity Models 771--800
Bruno Scarpa Comment on Article by Kim et al. . . . . 801--804
Joseph B. Stanford Comment on Article by Kim et al. . . . . 805--808
Sungduk Kim and
Rajeshwari Sundaram and
Germaine M. Buck Louis and
Cecilia Pyper Rejoinder . . . . . . . . . . . . . . . 809--812
Mingtao Ding and
Lihan He and
David Dunson and
Lawrence Carin Nonparametric Bayesian Segmentation of a
Multivariate Inhomogeneous Space-Time
Poisson Process . . . . . . . . . . . . 813--840
Cristian L. Bayes and
Jorge L. Bazán and
Catalina García A New Robust Regression Model for
Proportions . . . . . . . . . . . . . . 841--866
Hao Wang Bayesian Graphical Lasso Models and
Efficient Posterior Computation . . . . 867--886
Nicholas G. Polson and
James G. Scott On the Half-Cauchy Prior for a Global
Scale Parameter . . . . . . . . . . . . 887--902
Pierre Druilhet and
Denys Pommeret Invariant Conjugate Analysis for
Exponential Families . . . . . . . . . . 903--916
Joungyoun Kim and
Nicola M. Anthony and
Bret R. Larget A Bayesian Method for Estimating
Evolutionary History . . . . . . . . . . 917--974
Enrico Fabrizi and
Carlo Trivisano Bayesian Estimation of Log-Normal Means
with Finite Quadratic Expected Loss . . 975--996
John Paisley and
Chong Wang and
David M. Blei The Discrete Infinite Logistic Normal
Distribution . . . . . . . . . . . . . . 997--1034
Lin Huo and
Ying Yuan and
Guosheng Yin Bayesian Dose Finding for Combined Drugs
with Discrete and Continuous Doses . . . 1035--1052
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Daniel Schmidl and
Claudia Czado and
Sabine Hug and
Fabian J. Theis A Vine-copula Based Adaptive MCMC
Sampler for Efficient Inference of
Dynamical Systems . . . . . . . . . . . 1--22
Dawn B. Woodard Comment on Article by Schmidl et al. . . 23--26
Mark Girolami and
Antonietta Mira Comment on Article by Schmidl et al. . . 27--32
Daniel Schmidl and
Claudia Czado and
Sabine Hug and
Fabian J. Theis Rejoinder . . . . . . . . . . . . . . . 33--42
Francisco J. Rubio and
Mark F. J. Steel Bayesian Inference for $ P(X < Y) $ Using
Asymmetric Dependent Distributions . . . 43--62
Maria Anna Di Lucca and
Alessandra Guglielmi and
Peter Müller and
Fernando A. Quintana A Simple Class of Bayesian Nonparametric
Autoregression Models . . . . . . . . . 63--88
Charles Geyer and
Glen Meeden Asymptotics for Constrained Dirichlet
Distributions . . . . . . . . . . . . . 89--110
Jyotishka Datta and
Jayanta K. Ghosh Asymptotic Properties of Bayes Risk for
the Horseshoe Prior . . . . . . . . . . 111--132
Kiona Ogle and
Jarrett Barber and
Karla Sartor Feedback and Modularization in a
Bayesian Meta--analysis of Tree Traits
Affecting Forest Dynamics . . . . . . . 133--168
John Paul Gosling and
Andy Hart and
Helen Owen and
Michael Davies and
Jin Li and
Cameron MacKay A Bayes Linear Approach to
Weight-of-Evidence Risk Assessment for
Skin Allergy . . . . . . . . . . . . . . 169--186
Alain Desgagné Full Robustness in Bayesian Modelling of
a Scale Parameter . . . . . . . . . . . 187--220
Morris L. Eaton and
Robb J. Muirhead and
Adina I. Soaita On the Limiting Behavior of the
``Probability of Claiming Superiority''
in a Bayesian Context . . . . . . . . . 221--232
Eric Wang and
Esther Salazar and
David Dunson and
Lawrence Carin Spatio-Temporal Modeling of Legislation
and Votes . . . . . . . . . . . . . . . 233--268
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Peter Müller and
Riten Mitra Bayesian Nonparametric Inference -- Why
and How . . . . . . . . . . . . . . . . 269--302
Bradley P. Carlin and
Thomas A. Murray Comment on Article by Müller and Mitra 303--310
Peter D. Hoff Comment on Article by Müller and Mitra 311--318
Anthony O'Hagan Comment on Article by Müller and Mitra 319--322
Murray Aitken and
Julia Polak and
Julyan Arbel and
Bernardo Nipoti and
Bertrand S. Clarke and
Gregory E. Holt and
Andrew Gelman and
Miroslav Kárný and
Michalis Kolossiatis and
Athanasios Kottas and
Maria DeYoreo and
Valerie Poynor and
Susan M. Paddock and
Terrance D. Savitsky and
G. Parmigiani and
L. Trippa and
François Perron and
Christian P. Robert and
Judith Rousseau and
James G. Scott and
Surya T. Tokdar Contributed Discussion on Article by
Müller and Mitra . . . . . . . . . . . . 323--356
Peter Müller and
Riten Mitra Rejoinder . . . . . . . . . . . . . . . 357--360
Juan Antonio Cano and
Diego Salmerón Integral Priors and Constrained
Imaginary Training Samples for Nested
and Non-nested Bayesian Model Comparison 361--380
Cristiano C. Santos and
Rosangela H. Loschi and
Reinaldo B. Arellano-Valle Parameter Interpretation in Skewed
Logistic Regression with Random
Intercept . . . . . . . . . . . . . . . 381--410
Paul Fearnhead and
Benjamin M. Taylor An Adaptive Sequential Monte Carlo
Sampler . . . . . . . . . . . . . . . . 411--438
Alan Huang and
M. P. Wand Simple Marginally Noninformative Prior
Distributions for Covariance Matrices 439--452
Lane F. Burgette and
Jerome P. Reiter Multiple-Shrinkage Multinomial Probit
Models with Applications to Simulating
Geographies in Public Use Data . . . . . 453--478
Karthik Sriram and
R. V. Ramamoorthi and
Pulak Ghosh Posterior Consistency of Bayesian
Quantile Regression Based on the
Misspecified Asymmetric Laplace Density 479--504
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Marco Scutari On the Prior and Posterior Distributions
Used in Graphical Modelling . . . . . . 505--532
Adrian Dobra Comment on Article by Scutari . . . . . 533--538
Christine B. Peterson and
Francesco C. Stingo Comment on Article by Scutari . . . . . 539--542
Hao Wang Comment on Article by Scutari . . . . . 543--548
Marco Scutari Rejoinder . . . . . . . . . . . . . . . 549--552
Luai Al Labadi and
Mahmoud Zarepour On Asymptotic Properties and Almost Sure
Approximation of the Normalized
Inverse-Gaussian Process . . . . . . . . 553--568
Zeynep Baskurt and
Michael Evans Hypothesis Assessment and Inequalities
for Bayes Factors and Relative Belief
Ratios . . . . . . . . . . . . . . . . . 569--590
John R. Bryant and
Patrick J. Graham Bayesian Demographic Accounts:
Subnational Population Estimation Using
Multiple Data Sources . . . . . . . . . 591--622
Vanda Inácio de Carvalho and
Alejandro Jara and
Timothy E. Hanson and
Miguel de Carvalho Bayesian Nonparametric ROC Regression
Modeling . . . . . . . . . . . . . . . . 623--646
Jennifer Lynn Clarke and
Bertrand Clarke and
Chi-Wai Yu Prediction in $ \mathcal {M}$-complete
Problems with Limited Sample Size . . . 647--690
Jim E. Griffin and
Philip J. Brown Some Priors for Sparse Regression
Modelling . . . . . . . . . . . . . . . 691--702
Suyu Liu and
Jing Ning A Bayesian Dose-finding Design for Drug
Combination Trials with Delayed
Toxicities . . . . . . . . . . . . . . . 703--722
A. Jara and
L. E. Nieto-Barajas and
F. Quintana A Time Series Model for Responses on the
Unit Interval . . . . . . . . . . . . . 723--740
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Whole issue . . . . . . . . . . . . . . ??
Valen E. Johnson On Numerical Aspects of Bayesian Model
Selection in High and
Ultrahigh-dimensional Settings . . . . . 741--758
Yanxun Xu and
Juhee Lee and
Yuan Yuan and
Riten Mitra and
Shoudan Liang and
Peter Müller and
Yuan Ji Nonparametric Bayesian Bi-Clustering for
Next Generation Sequencing Count Data 759--780
Pierpaolo De Blasi and
Stephen G. Walker Bayesian Estimation of the Discrepancy
with Misspecified Parametric Models . . 781--800
Tamara Broderick and
Jim Pitman and
Michael I. Jordan Feature Allocations, Probability
Functions, and Paintboxes . . . . . . . 801--836
Tim Salimans and
David A. Knowles Fixed-Form Variational Posterior
Approximation through Stochastic Linear
Regression . . . . . . . . . . . . . . . 837--882
Qingzhao Yu and
Steven N. MacEachern and
Mario Peruggia Clustered Bayesian Model Averaging . . . 883--908
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Whole issue . . . . . . . . . . . . . . ??
Francisco J. Rubio and
Mark F. J. Steel Inference in Two-Piece Location-Scale
Models with Jeffreys Priors . . . . . . 1--22
José M. Bernardo Comment on Article by Rubio and Steel 23--24
James G. Scott Comment on Article by Rubio and Steel 25--28
Robert E. Weiss and
Marc A. Suchard Comment on Article by Rubio and Steel 29--38
Xinyi Xu Comment on Article by Rubio and Steel 39--44
Francisco J. Rubio and
Mark F. J. Steel Rejoinder . . . . . . . . . . . . . . . 45--52
Lorna M. Barclay and
Jane L. Hutton and
Jim Q. Smith Chain Event Graphs for Informed
Missingness . . . . . . . . . . . . . . 53--76
David A. Wooff Bayes Linear Sufficiency in
Non-exchangeable Multivariate Multiple
Regressions . . . . . . . . . . . . . . 77--96
Theodore Papamarkou and
Antonietta Mira and
Mark Girolami Zero Variance Differential Geometric
Markov Chain Monte Carlo Algorithms . . 97--128
Erlis Ruli and
Nicola Sartori and
Laura Ventura Marginal Posterior Simulation via
Higher-order Tail Area Approximations 129--146
Luis E. Nieto-Barajas and
Alberto Contreras-Cristán A Bayesian Nonparametric Approach for
Time Series Clustering . . . . . . . . . 147--170
Friederike Greb and
Tatyana Krivobokova and
Axel Munk and
Stephan von Cramon-Taubadel Regularized Bayesian Estimation of
Generalized Threshold Regression Models 171--196
Cristiano Villa and
Stephen G. Walker Objective Prior for the Number of
Degrees of Freedom of a t Distribution 197--220
Veronika Rockova and
Emmanuel Lesaffre Incorporating Grouping Information in
Bayesian Variable Selection with
Applications in Genomics . . . . . . . . 221--258
Anonymous Supplementary material . . . . . . . . . ??
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Whole issue . . . . . . . . . . . . . . ??
Zhihua Zhang and
Dakan Wang and
Guang Dai and
Michael I. Jordan Matrix-Variate Dirichlet Process Priors
with Applications . . . . . . . . . . . 259--286
Nammam A. Azadi and
Paul Fearnhead and
Gareth Ridall and
Joleen H. Blok Bayesian Sequential Experimental Design
for Binary Response Data with
Application to Electromyographic
Experiments . . . . . . . . . . . . . . 287--306
Juhee Lee and
Steven N. MacEachern and
Yiling Lu and
Gordon B. Mills Local-Mass Preserving Prior
Distributions for Nonparametric Bayesian
Models . . . . . . . . . . . . . . . . . 307--330
Ruitao Liu and
Arijit Chakrabarti and
Tapas Samanta and
Jayanta K. Ghosh and
Malay Ghosh On Divergence Measures Leading to
Jeffreys and Other Reference Priors . . 331--370
Xin-Yuan Song and
Jing-Heng Cai and
Xiang-Nan Feng and
Xue-Jun Jiang Bayesian Analysis of the
Functional-Coefficient Autoregressive
Heteroscedastic Model . . . . . . . . . 371--396
Yu Ryan Yue and
Daniel Simpson and
Finn Lindgren and
Håvard Rue Bayesian Adaptive Smoothing Splines
Using Stochastic Differential Equations 397--424
Jaakko Riihimäki and
Aki Vehtari Laplace Approximation for Logistic
Gaussian Process Density Estimation and
Regression . . . . . . . . . . . . . . . 425--448
Fei Liu and
Sounak Chakraborty and
Fan Li and
Yan Liu and
Aurelie C. Lozano Bayesian Regularization via Graph
Laplacian . . . . . . . . . . . . . . . 449--474
Catia Scricciolo Adaptive Bayesian Density Estimation in
$ L^p $-metrics with Pitman--Yor or
Normalized Inverse-Gaussian Process
Kernel Mixtures . . . . . . . . . . . . 475--520
Anonymous Supplementary material . . . . . . . . . ??
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Whole issue . . . . . . . . . . . . . . ??
Michael Finegold and
Mathias Drton Robust Bayesian Graphical Modeling Using
Dirichlet $t$-Distributions . . . . . . 521--550
François Caron and
Luke Bornn Comment on Article by Finegold and Drton 551--556
Babak Shahbaba Comment on Article by Finegold and Drton 557--560
Anonymous Contributed Discussion on Article by
Finegold and Drton . . . . . . . . . . . 561--590
Michael Finegold and
Mathias Drton Rejoinder . . . . . . . . . . . . . . . 591--596
Timothy E. Hanson and
Adam J. Branscum and
Wesley O. Johnson Informative $g$-Priors for Logistic
Regression . . . . . . . . . . . . . . . 597--612
George Casella and
Elías Moreno and
F. Javier Girón Cluster Analysis, Model Selection, and
Prior Distributions on Models . . . . . 613--658
A. Marie Fitch and
M. Beatrix Jones and
Hél\`ene Massam The Performance of Covariance Selection
Methods That Consider Decomposable
Models Only . . . . . . . . . . . . . . 659--684
Joseph B. Kadane and
Steven N. MacEachern Toward Rational Social Decisions: A
Review and Some Results . . . . . . . . 685--698
Kuo-Jung Lee and
Galin L. Jones and
Brian S. Caffo and
Susan S. Bassett Spatial Bayesian Variable Selection
Models on Functional Magnetic Resonance
Imaging Time-Series Data . . . . . . . . 699--732
Meng Li and
Subhashis Ghosal Bayesian Multiscale Smoothing of
Gaussian Noised Images . . . . . . . . . 733--758
Jesse Windle and
Carlos M. Carvalho A Tractable State-Space Model for
Symmetric Positive-Definite Matrices . . 759--792
Roberto Casarin Comment on Article by Windle and
Carvalho . . . . . . . . . . . . . . . . 793--804
Catherine Scipione Forbes Comment on Article by Windle and
Carvalho . . . . . . . . . . . . . . . . 805--808
Enrique ter Horst and
German Molina Comment on Article by Windle and
Carvalho . . . . . . . . . . . . . . . . 809--818
Jesse Windle and
Carlos M. Carvalho Rejoinder . . . . . . . . . . . . . . . 819--822
Asael Fabian Martínez and
Ramsés H. Mena On a Nonparametric Change Point
Detection Model in Markovian Regimes . . 823--858
Eduard Belitser and
Paulo Serra Adaptive Priors Based on Splines with
Random Knots . . . . . . . . . . . . . . 859--882
Henrik Nyman and
Johan Pensar and
Timo Koski and
Jukka Corander Stratified Graphical Models ---
Context-Specific Independence in
Graphical Models . . . . . . . . . . . . 883--908
David Shalloway The Evidentiary Credible Region . . . . 909--922
Arkady Shemyakin Hellinger Distance and Non-informative
Priors . . . . . . . . . . . . . . . . . 923--938
Isabelle Smith and
André Ferrari Equivalence between the Posterior
Distribution of the Likelihood Ratio and
a $p$-value in an Invariant Frame . . . 939--962
Linda S. L. Tan and
David J. Nott A Stochastic Variational Framework for
Fitting and Diagnosing Generalized
Linear Mixed Models . . . . . . . . . . 963--1004
Trevelyan J. McKinley and
Michelle Morters and
James L. N. Wood Bayesian Model Choice in Cumulative Link
Ordinal Regression Models . . . . . . . 1--30
Fumiyasu Komaki Asymptotic Properties of Bayesian
Predictive Densities When the
Distributions of Data and Target
Variables are Different . . . . . . . . 31--51
Harold Bae and
Thomas Perls and
Martin Steinberg and
Paola Sebastiani Bayesian Polynomial Regression Models to
Fit Multiple Genetic Models for
Quantitative Traits . . . . . . . . . . 53--74
Dimitris Fouskakis and
Ioannis Ntzoufras and
David Draper Power-Expected-Posterior Priors for
Variable Selection in Gaussian Linear
Models . . . . . . . . . . . . . . . . . 75--107
A. Mohammadi and
E. C. Wit Bayesian Structure Learning in Sparse
Gaussian Graphical Models . . . . . . . 109--138
Cyr Emile M'lan and
Ming-Hui Chen Objective Bayesian Inference for
Bilateral Data . . . . . . . . . . . . . 139--170
Fernando V. Bonassi and
Mike West Sequential Monte Carlo with Adaptive
Weights for Approximate Bayesian
Computation . . . . . . . . . . . . . . 171--187
James O. Berger and
Jose M. Bernardo and
Dongchu Sun Overall Objective Priors . . . . . . . . 189--221
Siva Sivaganesan Comment on Article by Berger, Bernardo,
and Sun . . . . . . . . . . . . . . . . 223--226
Manuel Mendoza and
Eduardo Gutiérrez-Peña Comment on Article by Berger, Bernardo,
and Sun . . . . . . . . . . . . . . . . 227--231
Judith Rousseau Comment on Article by Berger, Bernardo,
and Sun . . . . . . . . . . . . . . . . 233--236
Gauri Sankar Datta and
Brunero Liseo Comment on Article by Berger, Bernardo,
and Sun . . . . . . . . . . . . . . . . 237--241
James O. Berger and
Jose M. Bernardo and
Dongchu Sun Rejoinder . . . . . . . . . . . . . . . 243--246
Zhihua Zhang and
Jin Li Compound Poisson Processes, Latent
Shrinkage Priors and Bayesian Nonconvex
Penalization . . . . . . . . . . . . . . 247--274
Stanley I. M. Ko and
Terence T. L. Chong and
Pulak Ghosh Dirichlet Process Hidden Markov Multiple
Change-point Model . . . . . . . . . . . 275--296
Chris C. Holmes and
François Caron and
Jim E. Griffin and
David A. Stephens Two-sample Bayesian Nonparametric
Hypothesis Testing . . . . . . . . . . . 297--320
Ma\lgorzata Roos and
Thiago G. Martins and
Leonhard Held and
Håvard Rue Sensitivity Analysis for Bayesian
Hierarchical Models . . . . . . . . . . 321--349
Hao Wang Scaling It Up: Stochastic Search
Structure Learning in Graphical Models 351--377
Garritt L. Page and
Fernando A. Quintana Predictions Based on the Clustering of
Heterogeneous Functions via Shape and
Subject-Specific Covariates . . . . . . 379--410
Stefano Cabras and
Maria Eugenia Castellanos Nueda and
Erlis Ruli Approximate Bayesian Computation by
Modelling Summary Statistics in a
Quasi-likelihood Framework . . . . . . . 411--439
Lilia Costa and
Jim Smith and
Thomas Nichols and
James Cussens and
Eugene P. Duff and
Tamar R. Makin Searching Multiregression Dynamic Models
of Resting-State fMRI Networks Using
Integer Programming . . . . . . . . . . 441--478
A. Philip Dawid and
Monica Musio Bayesian Model Selection Based on Proper
Scoring Rules . . . . . . . . . . . . . 479--499
Matthias Katzfuss and
Anirban Bhattacharya Comment on Article by Dawid and Musio 501--504
Christopher M. Hans and
Mario Peruggia Comment on Article by Dawid and Musio 505--509
C. Grazian and
I. Masiani and
C. P. Robert Comment on Article by Dawid and Musio 511--515
A. Philip Dawid and
Monica Musio Rejoinder . . . . . . . . . . . . . . . 517--521
Sergio Venturini and
Francesca Dominici and
Giovanni Parmigiani Generalized Quantile Treatment Effect: A
Flexible Bayesian Approach Using
Quantile Ratio Smoothing . . . . . . . . 523--552
Mauro Bernardi and
Ghislaine Gayraud and
Lea Petrella Bayesian Tail Risk Interdependence Using
Quantile Regression . . . . . . . . . . 553--603
Yajuan Si and
Natesh S. Pillai and
Andrew Gelman Bayesian Nonparametric Weighted Sampling
Inference . . . . . . . . . . . . . . . 605--625
Douglas K. Sparks and
Kshitij Khare and
Malay Ghosh Necessary and Sufficient Conditions for
High-Dimensional Posterior Consistency
under $g$-Priors . . . . . . . . . . . . 627--664
Maxim Panov and
Vladimir Spokoiny Finite Sample Bernstein--von Mises
Theorem for Semiparametric Problems . . 665--710
Gustavo da Silva Ferreira and
Dani Gamerman Optimal Design in Geostatistics under
Preferential Sampling . . . . . . . . . 711--735
Michael Chipeta and
Peter J. Diggle Comment on Article by Ferreira and
Gamerman . . . . . . . . . . . . . . . . 737--739
Noel Cressie and
Raymond L. Chambers Comment on Article by Ferreira and
Gamerman . . . . . . . . . . . . . . . . 741--748
James V. Zidek Comment on Article by Ferreira and
Gamerman . . . . . . . . . . . . . . . . 749--752
Gustavo da Silva Ferreira and
Dani Gamerman Rejoinder . . . . . . . . . . . . . . . 753--758
R. V. Ramamoorthi and
Karthik Sriram and
Ryan Martin On Posterior Concentration in
Misspecified Models . . . . . . . . . . 759--789
Roberto Casarin and
Fabrizio Leisen and
German Molina and
Enrique ter Horst A Bayesian Beta Markov Random Field
Calibration of the Term Structure of
Implied Risk Neutral Densities . . . . . 791--819
Maria DeYoreo and
Athanasios Kottas A Fully Nonparametric Modeling Approach
to Binary Regression . . . . . . . . . . 821--847
Rebecca C. Steorts Entity Resolution with Empirically
Motivated Priors . . . . . . . . . . . . 849--875
Daniel Williamson and
Michael Goldstein Posterior Belief Assessment: Extracting
Meaningful Subjective Judgements from
Bayesian Analyses with Complex
Statistical Models . . . . . . . . . . . 877--908
Mohammad Arshad Rahman Bayesian Quantile Regression for Ordinal
Models . . . . . . . . . . . . . . . . . 1--24
O. Bodnar and
A. Link and
C. Elster Objective Bayesian Inference for a
Generalized Marginal Random Effects
Model . . . . . . . . . . . . . . . . . 25--45
Daniel J. Graham and
Emma J. McCoy and
David A. Stephens Approximate Bayesian Inference for
Doubly Robust Estimation . . . . . . . . 47--69
Adam Justin Suarez and
Subhashis Ghosal Bayesian Clustering of Functional Data
Using Local Features . . . . . . . . . . 71--98
Riten Mitra and
Peter Müller and
Yuan Ji Bayesian Graphical Models for
Differential Pathways . . . . . . . . . 99--124
Lutz Gruber and
Mike West GPU-Accelerated Bayesian Learning and
Forecasting in Simultaneous Graphical
Dynamic Linear Models . . . . . . . . . 125--149
Sophie Donnet and
Judith Rousseau Bayesian Inference for Partially
Observed Multiplicative Intensity
Processes . . . . . . . . . . . . . . . 151--190
Brian P. Weaver and
Brian J. Williams and
Christine M. Anderson-Cook and
David M. Higdon Computational Enhancements to Bayesian
Design of Experiments Using Gaussian
Processes . . . . . . . . . . . . . . . 191--213
Nial Friel and
Antonietta Mira and
Chris. J. Oates Exploiting Multi-Core Architectures for
Reduced-Variance Estimation with
Intractable Likelihoods . . . . . . . . 215--245
Jie Xiong and
Väinö Jääskinen and
Jukka Corander Recursive Learning for Sparse Markov
Models . . . . . . . . . . . . . . . . . 247--263
Garritt L. Page and
Fernando A. Quintana Spatial Product Partition Models . . . . 265--298
Robert B. Gramacy and
Herbert K. H. Lee Comment on Article by Page and Quintana 299--302
Brian J. Reich and
Montserrat Fuentes Comment on Article by Page and Quintana 303--305
Carlo Gaetan and
Simone A. Padoan and
Igor Prünster Comment on Article by Page and Quintana 307--314
Garrit L. Page and
Fernando A. Quintana Rejoinder . . . . . . . . . . . . . . . 315--323
Christopher C. Drovandi and
Anthony N. Pettitt and
Roy A. McCutchan Exact and Approximate Bayesian Inference
for Low Integer-Valued Time Series
Models with Intractable Likelihoods . . 325--352
Hongmei Zhang and
Xianzheng Huang and
Jianjun Gan and
Wilfried Karmaus and
Tara Sabo-Attwood A Two-Component $G$-Prior for Variable
Selection . . . . . . . . . . . . . . . 353--380
Thomas A. Murray and
Brian P. Hobbs and
Daniel J. Sargent and
Bradley P. Carlin Flexible Bayesian Survival Modeling with
Semiparametric Time-Dependent and
Shape-Restricted Covariate Effects . . . 381--402
Joseph B. Kadane Sums of Possibly Associated Bernoulli
Variables: The Conway--Maxwell-Binomial
Distribution . . . . . . . . . . . . . . 403--420
Hwan-sik Choi Expert Information and Nonparametric
Bayesian Inference of Rare Events . . . 421--445
Wen Cheng and
Ian L. Dryden and
Xianzheng Huang Bayesian Registration of Functions and
Curves . . . . . . . . . . . . . . . . . 447--475
Mengjie Chen and
Chao Gao and
Hongyu Zhao Posterior Contraction Rates of the
Phylogenetic Indian Buffet Processes . . 477--497
Tracy A. Schifeling and
Jerome P. Reiter Incorporating Marginal Prior Information
in Latent Class Models . . . . . . . . . 499--518
Kelly C. M. Gonçalves and
Fernando A. S. Moura A Mixture Model for Rare and Clustered
Populations Under Adaptive Cluster
Sampling . . . . . . . . . . . . . . . . 519--544
Sarah E. Michalak and
Carl N. Morris Posterior Propriety for Hierarchical
Models with Log-Likelihoods That Have
Norm Bounds . . . . . . . . . . . . . . 545--571
Jeong Eun Lee and
Christian P. Robert Importance Sampling Schemes for Evidence
Approximation in Mixture Models . . . . 573--597
Zhuqing Liu and
Veronica J. Berrocal and
Andreas J. Bartsch and
Timothy D. Johnson Pre-surgical fMRI Data Analysis Using a
Spatially Adaptive Conditionally
Autoregressive Model . . . . . . . . . . 599--625
Peter D. Hoff Equivariant and Scale-Free Tucker
Decomposition Models . . . . . . . . . . 627--648
Jingjing Yang and
Hongxiao Zhu and
Taeryon Choi and
Dennis D. Cox Smoothing and Mean-Covariance Estimation
of Functional Data with a Bayesian
Hierarchical Model . . . . . . . . . . . 649--670
Alen Alexanderian and
Philip J. Gloor and
Omar Ghattas On Bayesian $A$- and $D$-Optimal
Experimental Designs in Infinite
Dimensions . . . . . . . . . . . . . . . 671--695
S. Favaro and
A. Lijoi and
C. Nava and
B. Nipoti and
I. Prünster and
Y. W. Teh On the Stick-Breaking Representation for
Homogeneous NRMIs . . . . . . . . . . . 697--724
A. Philip Dawid and
Monica Musio and
Stephen E. Fienberg From Statistical Evidence to Evidence of
Causality . . . . . . . . . . . . . . . 725--752
Prasenjit Ghosh and
Xueying Tang and
Malay Ghosh and
Arijit Chakrabarti Asymptotic Properties of Bayes Risk of a
General Class of Shrinkage Priors in
Multiple Hypothesis Testing Under
Sparsity . . . . . . . . . . . . . . . . 753--796
Tony Pourmohamad and
Herbert K. H. Lee Multivariate Stochastic Process Models
for Correlated Responses of Mixed Type 797--820
Ruibin Xi and
Yunxiao Li and
Yiming Hu Bayesian Quantile Regression Based on
the Empirical Likelihood with Spike and
Slab Priors . . . . . . . . . . . . . . 821--855
Caitríona M. Ryan and
Christopher C. Drovandi and
Anthony N. Pettitt Optimal Bayesian Experimental Design for
Models with Intractable Likelihoods
Using Indirect Inference Applied to
Biological Process Models . . . . . . . 857--883
Matthew T. Pratola Efficient Metropolis--Hastings Proposal
Mechanisms for Bayesian Regression Tree
Models . . . . . . . . . . . . . . . . . 885--911
Robert B. Gramacy Comment on Article by Pratola . . . . . 913--919
Christopher M. Hans Comment on Article by Pratola . . . . . 921--927
Oksana A. Chkrebtii and
Scotland Leman and
Andrew Hoegh and
Reihaneh Entezari and
Radu V. Craiu and
Jeffrey S. Rosenthal and
Abdolreza Mohammadi and
Maurits Kaptein and
Luca Martino and
Rafael B. Stern and
Francisco Louzada Contributed Discussion on Article by
Pratola . . . . . . . . . . . . . . . . 929--943
Matthew T. Pratola Rejoinder . . . . . . . . . . . . . . . 945--955
Anonymous Editorial Board . . . . . . . . . . . . ??
Anonymous Table of Contents . . . . . . . . . . . ??
Chin-I. Cheng and
Paul L. Speckman Bayes Factors for Smoothing Spline ANOVA 957--975
Wen-Hsi Yang and
Scott H. Holan and
Christopher K. Wikle Bayesian Lattice Filters for
Time-Varying Autoregression and
Time-Frequency Analysis . . . . . . . . 977--1003
N. T. Underhill and
J. Q. Smith Context-Dependent Score Based Bayesian
Information Criteria . . . . . . . . . . 1005--1033
Samantha Leorato and
Maura Mezzetti Spatial Panel Data Model with Error
Dependence: A Bayesian Separable
Covariance Approach . . . . . . . . . . 1035--1069
Nadja Klein and
Thomas Kneib Scale-Dependent Priors for Variance
Parameters in Structured Additive
Distributional Regression . . . . . . . 1071--1106
J. Pablo Arias-Nicolás and
Fabrizio Ruggeri and
Alfonso Suárez-Llorens New Classes of Priors Based on
Stochastic Orders and Distortion
Functions . . . . . . . . . . . . . . . 1107--1136
Seonghyun Jeong and
Taeyoung Park Bayesian Semiparametric Inference on
Functional Relationships in Linear Mixed
Models . . . . . . . . . . . . . . . . . 1137--1163
Rodrigo A. Collazo and
Jim Q. Smith A New Family of Non-Local Priors for
Chain Event Graph Model Selection . . . 1165--1201
Tingting Zhao and
Ziyu Wang and
Alexander Cumberworth and
Joerg Gsponer and
Nando de Freitas and
Alexandre Bouchard-Côté Bayesian Analysis of Continuous Time
Markov Chains with Application to
Phylogenetic Modelling . . . . . . . . . 1203--1237
Oksana A. Chkrebtii and
David A. Campbell and
Ben Calderhead and
Mark A. Girolami Bayesian Solution Uncertainty
Quantification for Differential
Equations . . . . . . . . . . . . . . . 1239--1267
Martin Lysy Comment on Article by Chkrebtii,
Campbell, Calderhead, and Girolami . . . 1269--1273
Sarat C. Dass Comment on Article by Chkrebtii,
Campbell, Calderhead, and Girolami . . . 1275--1277
Bani K. Mallick and
Keren Yang and
Nilabja Guha and
Yalchin Efendiev Comment on Article by Chkrebtii,
Campbell, Calderhead, and Girolami . . . 1279--1284
François-Xavier Briol and
Jon Cockayne and
Onur Teymur and
William Weimin Yoo and
Jon Cockayne and
Michael Schober and
Philipp Hennig Contributed Discussion on Article by
Chkrebtii, Campbell, Calderhead, and
Girolami . . . . . . . . . . . . . . . . 1285--1293
Oksana A. Chkrebtii and
David A. Campbell and
Ben Calderhead and
Mark A. Girolami Rejoinder . . . . . . . . . . . . . . . 1295--1299
Anonymous Editorial Board . . . . . . . . . . . . ??
Anonymous Table of Contents . . . . . . . . . . . ??
Thomas J. Leininger and
Alan E. Gelfand Bayesian Inference and Model Assessment
for Spatial Point Patterns Using
Posterior Predictive Samples . . . . . . 1--30
Haolun Shi and
Guosheng Yin Bayesian Two-Stage Design for Phase II
Clinical Trials with Switching
Hypothesis Tests . . . . . . . . . . . . 31--51
Sophie Donnet and
Vincent Rivoirard and
Judith Rousseau and
Catia Scricciolo Posterior Concentration Rates for
Counting Processes with Aalen
Multiplicative Intensities . . . . . . . 53--87
Bo Jiang and
Chao Ye and
Jun S. Liu Bayesian Nonparametric Tests via Sliced
Inverse Modeling . . . . . . . . . . . . 89--112
Daniel Hernandez-Stumpfhauser and
F. Jay Breidt and
Mark J. van der Woerd The General Projected Normal
Distribution of Arbitrary Dimension:
Modeling and Bayesian Inference . . . . 113--133
Jim Griffin and
Phil Brown Hierarchical Shrinkage Priors for
Regression Models . . . . . . . . . . . 135--159
Genya Kobayashi Bayesian Endogenous Tobit Quantile
Regression . . . . . . . . . . . . . . . 161--191
Lawrence Bardwell and
Paul Fearnhead Bayesian Detection of Abnormal Segments
in Multiple Time Series . . . . . . . . 193--218
Paulo Serra and
Tatyana Krivobokova Adaptive Empirical Bayesian Smoothing
Splines . . . . . . . . . . . . . . . . 219--238
Miguel A. Martinez-Beneito and
Paloma Botella-Rocamora and
Sudipto Banerjee Towards a Multidimensional Approach to
Bayesian Disease Mapping . . . . . . . . 239--259
Anna Pajor Estimating the Marginal Likelihood Using
the Arithmetic Mean Identity . . . . . . 261--287
Dennis Prangle Adapting the ABC Distance Function . . . 289--309
Adam J. Suarez and
Subhashis Ghosal Bayesian Estimation of Principal
Components for Functional Data . . . . . 311--333
Bin Zhu and
David B. Dunson Bayesian Functional Data Modeling for
Heterogeneous Volatility . . . . . . . . 335--350
Daniel K. Sewell and
Yuguo Chen Latent Space Approaches to Community
Detection in Dynamic Networks . . . . . 351--377
Seongil Jo and
Jaeyong Lee and
Peter Müller and
Fernando A. Quintana and
Lorenzo Trippa Dependent Species Sampling Models for
Spatial Density Estimation . . . . . . . 379--406
Fabrizio Ruggeri and
Zaid Sawlan and
Marco Scavino and
Raul Tempone A Hierarchical Bayesian Setting for an
Inverse Problem in Linear Parabolic PDEs
with Noisy Boundary Conditions . . . . . 407--433
Gavin A. Whitaker and
Andrew Golightly and
Richard J. Boys and
Chris Sherlock Bayesian Inference for Diffusion-Driven
Mixed-Effects Models . . . . . . . . . . 435--463
Daniel Turek and
Perry de Valpine and
Christopher J. Paciorek and
Clifford Anderson-Bergman Automated Parameter Blocking for
Efficient Markov Chain Monte Carlo
Sampling . . . . . . . . . . . . . . . . 465--490
Osvaldo Anacleto and
Catriona Queen Dynamic Chain Graph Models for Time
Series Network Data . . . . . . . . . . 491--509
Min Wang Mixtures of $g$-Priors for Analysis of
Variance Models with a Diverging Number
of Parameters . . . . . . . . . . . . . 511--532
Hyungsuk Tak and
Carl N. Morris Data-Dependent Posterior Propriety of a
Bayesian Beta-Binomial-Logit Model . . . 533--555
Cecilia Earls and
Giles Hooker Variational Bayes for Functional Data
Registration, Smoothing, and Prediction 557--582
Sudipto Banerjee High-Dimensional Bayesian Geostatistics 583--614
María-Eglée Pérez and
Luis Raúl Pericchi and
Isabel Cristina Ramírez The Scaled Beta2 Distribution as a
Robust Prior for Scales . . . . . . . . 615--637
Yanxun Xu and
Peter F. Thall and
Peter Müller and
Mehran J. Reza A Decision-Theoretic Comparison of
Treatments to Resolve Air Leaks After
Lung Surgery Based on Nonparametric
Modeling . . . . . . . . . . . . . . . . 639--652
Jeffrey D. Hart and
Taeryon Choi Nonparametric Goodness of Fit via
Cross-Validation Bayes Factors . . . . . 653--677
Maria DeYoreo and
Jerome P. Reiter and
D. Sunshine Hillygus Bayesian Mixture Models with Focused
Clustering for Mixed Ordinal and Nominal
Data . . . . . . . . . . . . . . . . . . 679--703
Luai Al Labadi and
Michael Evans Optimal Robustness Results for Relative
Belief Inferences and the Relationship
to Prior-Data Conflict . . . . . . . . . 705--728
Silvia Polettini A Generalised Semiparametric Bayesian
Fay--Herriot Model for Small Area
Estimation Shrinking Both Means and
Variances . . . . . . . . . . . . . . . 729--752
Vivekananda Roy and
Sounak Chakraborty Selection of Tuning Parameters, Solution
Paths and Standard Errors for Bayesian
Lassos . . . . . . . . . . . . . . . . . 753--778
Li Ma Adaptive Shrinkage in Pólya Tree Type
Models . . . . . . . . . . . . . . . . . 779--805
Tri Le and
Bertrand Clarke A Bayes Interpretation of Stacking for
$M$-Complete and $M$-Open Settings . . . 807--829
Zach Shahn and
David Madigan Latent Class Mixture Models of Treatment
Effect Heterogeneity . . . . . . . . . . 831--854
Daniel Taylor-Rodríguez and
Andrew J. Womack and
Claudio Fuentes and
Nikolay Bliznyuk Intrinsic Bayesian Analysis for
Occupancy Models . . . . . . . . . . . . 855--877
Nalan Bastürk and
Lennart Hoogerheide and
Herman K. van Dijk Bayesian Analysis of Boundary and
Near-Boundary Evidence in Econometric
Models with Reduced Rank . . . . . . . . 879--917
Sarah Filippi and
Chris C. Holmes A Bayesian Nonparametric Approach to
Testing for Dependence Between Random
Variables . . . . . . . . . . . . . . . 919--938
Daniel Taylor-Rodríguez and
Kimberly Kaufeld and
Erin M. Schliep and
James S. Clark and
Alan E. Gelfand Joint Species Distribution Modeling:
Dimension Reduction Using Dirichlet
Processes . . . . . . . . . . . . . . . 939--967
David Puelz and
P. Richard Hahn and
Carlos M. Carvalho Variable Selection in Seemingly
Unrelated Regressions with Random
Predictors . . . . . . . . . . . . . . . 969--989
Clara Grazian and
Brunero Liseo Approximate Bayesian Inference in
Semiparametric Copula Models . . . . . . 991--1016
Yulai Cong and
Bo Chen and
Mingyuan Zhou Fast Simulation of Hyperplane-Truncated
Multivariate Normal Distributions . . . 1017--1037
B. Liquet and
K. Mengersen and
A. N. Pettitt and
M. Sutton Bayesian Variable Selection Regression
of Multivariate Responses for Group Data 1039--1067
Peter Grünwald and
Thijs van Ommen Inconsistency of Bayesian Inference for
Misspecified Linear Models, and a
Proposal for Repairing It . . . . . . . 1069--1103
Anindya Bhadra and
Jyotishka Datta and
Nicholas G. Polson and
Brandon Willard The Horseshoe+ Estimator of Ultra-Sparse
Signals . . . . . . . . . . . . . . . . 1105--1131
Prasenjit Ghosh and
Arijit Chakrabarti Asymptotic Optimality of One-Group
Shrinkage Priors in Sparse
High-dimensional Problems . . . . . . . 1133--1161
P. Ramírez-Cobo and
R. E. Lillo and
M. P. Wiper Bayesian Analysis of the Stationary
MAP$_2$ . . . . . . . . . . . . . . . . 1163--1194
Johan Pensar and
Henrik Nyman and
Juha Niiranen and
Jukka Corander Marginal Pseudo-Likelihood Learning of
Discrete Markov Network Structures . . . 1195--1215
Karthik Sriram and
R. V. Ramamoorthi Correction to: ``Posterior Consistency
of Bayesian Quantile Regression Based on
the Misspecified Asymmetric Laplace
Density'' . . . . . . . . . . . . . . . 1217--1219
Stéphanie van der Pas and
Botond Szabó and
Aad van der Vaart Uncertainty Quantification for the
Horseshoe (with Discussion) . . . . . . 1221--1274
Nicholas G. Polson and
Vadim Sokolov Deep Learning: A Bayesian Perspective 1275--1304
Maria A. Terres and
Montserrat Fuentes and
Dean Hesterberg and
Matthew Polizzotto Bayesian Spectral Modeling for
Multivariate Spatial Distributions of
Elemental Concentrations in Soil . . . . 1--28
Daniele Durante and
David B. Dunson Bayesian Inference and Testing of Group
Differences in Brain Networks . . . . . 29--58
David J. Nott and
Christopher C. Drovandi and
Kerrie Mengersen and
Michael Evans Approximation of Bayesian Predictive . . 59--83
Sindhu Ghanta and
Jennifer G. Dy and
Donglin Niu and
Michael I. Jordan Latent Marked Poisson Process with
Applications to Object Segmentation . . 85--113
Ruby Chiu-Hsing Weng and
D. Stephen Coad Real-Time Bayesian Parameter Estimation
for Item Response Models . . . . . . . . 115--137
Christopher C. Drovandi and
Minh-Ngoc Tran Improving the Efficiency of Fully
Bayesian Optimal Design of Experiments
Using Randomised Quasi-Monte Carlo . . . 139--162
P. Richard Hahn and
Carlos M. Carvalho and
David Puelz and
Jingyu He Regularization and Confounding in Linear
Regression for Treatment Effect
Estimation . . . . . . . . . . . . . . . 163--182
Jingchen Hu and
Jerome P. Reiter and
Quanli Wang Dirichlet Process Mixture Models for
Modeling and Generating Synthetic
Versions of Nested Categorical Data . . 183--200
James Johndrow and
Anirban Bhattacharya Optimal Gaussian Approximations to the
Posterior for Log-Linear Models with
Diaconis--Ylvisaker Priors . . . . . . . 201--223
James R. Faulkner and
Vladimir N. Minin Locally Adaptive Smoothing with Markov
Random Fields and Shrinkage Priors . . . 225--252
Jonathan R. Bradley and
Scott H. Holan and
Christopher K. Wikle Computationally Efficient Multivariate
Spatio-Temporal Models for
High-Dimensional Count-Valued Data (with
Discussion) . . . . . . . . . . . . . . 253--310
Yu-Bo Wang and
Ming-Hui Chen and
Lynn Kuo and
Paul O. Lewis A New Monte Carlo Method for Estimating
Marginal Likelihoods . . . . . . . . . . 311--333
Daniel A. Henderson and
Liam J. Kirrane A Comparison of Truncated and
Time-Weighted Plackett--Luce Models for
Probabilistic Forecasting of Formula One
Results . . . . . . . . . . . . . . . . 335--358
Joyee Ghosh and
Yingbo Li and
Robin Mitra On the Use of Cauchy Prior Distributions
for Bayesian Logistic Regression . . . . 359--383
Tevfik Aktekin and
Nick Polson and
Refik Soyer Sequential Bayesian Analysis of
Multivariate Count Data . . . . . . . . 385--409
Lili Zhao and
Weisheng Wu and
Dai Feng and
Hui Jiang and
XuanLong Nguyen Bayesian Analysis of RNA-Seq Data Using
a Family of Negative Binomial Models . . 411--436
Panayiota Touloupou and
Naif Alzahrani and
Peter Neal and
Simon E. F. Spencer and
Trevelyan J. McKinley Efficient Model Comparison Techniques
for Models Requiring Large Scale Data
Augmentation . . . . . . . . . . . . . . 437--459
Jean-Bernard Salomond Testing Un-Separated Hypotheses by
Estimating a Distance . . . . . . . . . 461--484
Cheng Zhang and
Babak Shahbaba and
Hongkai Zhao Variational Hamiltonian Monte Carlo via
Score Matching . . . . . . . . . . . . . 485--506
Christopher Nemeth and
Chris Sherlock Merging MCMC Subposteriors through
Gaussian-Process Approximations . . . . 507--530
Hamid Zareifard and
Majid Jafari Khaledi and
Firoozeh Rivaz and
Mohammad Q. Vahidi-Asl Modeling Skewed Spatial Data Using a
Convolution of Gaussian and Log-Gaussian
Processes . . . . . . . . . . . . . . . 531--557
Sara Wade and
Zoubin Ghahramani Bayesian Cluster Analysis: Point
Estimation and Credible Balls (with
Discussion) . . . . . . . . . . . . . . 559--626
Guido Consonni and
Dimitris Fouskakis and
Brunero Liseo and
Ioannis Ntzoufras Prior Distributions for Objective
Bayesian Analysis . . . . . . . . . . . 627--679
Laura Forastiere and
Fabrizia Mealli and
Luke Miratrix Posterior Predictive $p$-Values with
Fisher Randomization Tests in
Noncompliance Settings: Test Statistics
vs Discrepancy Measures . . . . . . . . 681--701
Zacharie Naulet and
Éric Barat Some Aspects of Symmetric Gamma Process
Mixtures . . . . . . . . . . . . . . . . 703--720
Dimitris Fouskakis and
Ioannis Ntzoufras and
Konstantinos Perrakis Power-Expected-Posterior Priors for
Generalized Linear Models . . . . . . . 721--748
Kevin James Wilson Specification of Informative Prior
Distributions for Multinomial Models
Using Vine Copulas . . . . . . . . . . . 749--766
S. L. van der Pas and
A. W. van der Vaart Bayesian Community Detection . . . . . . 767--796
Alexander Y. Shestopaloff and
Radford M. Neal Sampling Latent States for
High-Dimensional Non-Linear State Space
Models with the Embedded HMM Method . . 797--822
Yan Zhang and
Howard D. Bondell Variable Selection via Penalized
Credible Regions with Dirichlet--Laplace
Global-Local Shrinkage Priors . . . . . 823--844
Sonia Migliorati and
Agnese Maria Di Brisco and
Andrea Ongaro A New Regression Model for Bounded
Responses . . . . . . . . . . . . . . . 845--872
Edward Higson and
Will Handley and
Mike Hobson and
Anthony Lasenby Sampling Errors in Nested Sampling
Parameter Estimation . . . . . . . . . . 873--896
Jim Griffin and
Fabrizio Leisen Modelling and Computation Using NCoRM
Mixtures for Density Regression . . . . 897--916
Yuling Yao and
Aki Vehtari and
Daniel Simpson and
Andrew Gelman Using Stacking to Average Bayesian
Predictive Distributions (with
Discussion) . . . . . . . . . . . . . . 917--1007
Joseph B. Kadane and
Galit Shmueli and
Thomas P. Minka and
Sharad Borle and
Peter Boatwright Note of correction: ``Conjugate Analysis
of the Conway--Maxwell--Poisson
Distribution'' . . . . . . . . . . . . . 1009--1009
Hang Qian Big Data Bayesian Linear Regression and
Variable Selection by
Normal-Inverse-Gamma Summation . . . . . 1011--1035
Yuttapong Thawornwattana and
Daniel Dalquen and
Ziheng Yang Designing Simple and Efficient Markov
Chain Monte Carlo Proposal Kernels . . . 1037--1063
Mingyuan Zhou Nonparametric Bayesian Negative Binomial
Factor Analysis . . . . . . . . . . . . 1065--1093
Yang Ni and
Yuan Ji and
Peter Müller Reciprocal Graphical Models for
Integrative Gene Regulatory Network
Analysis . . . . . . . . . . . . . . . . 1095--1110
Lutz F. Gruber and
Claudia Czado Bayesian Model Selection of Regular Vine
Copulas . . . . . . . . . . . . . . . . 1111--1135
Biao Yang and
Jonathan R. Stroud and
Gabriel Huerta Sequential Monte Carlo Smoothing with
Parameter Estimation . . . . . . . . . . 1137--1161
Chong Wang and
David M. Blei A General Method for Robust Bayesian
Modeling . . . . . . . . . . . . . . . . 1163--1191
Joris Mulder and
Luis Raúl Pericchi The Matrix-$F$ Prior for Estimating and
Testing Covariance Matrices . . . . . . 1193--1214
Kyoungjae Lee and
Jaeyong Lee Optimal Bayesian Minimax Rates for
Unconstrained Large Covariance Matrices 1215--1233
Federico Castelletti and
Guido Consonni and
Marco L. Della Vedova and
Stefano Peluso Learning Markov Equivalence Classes of
Directed Acyclic Graphs: An Objective
Bayes Approach . . . . . . . . . . . . . 1235--1260
Martin Bezener and
John Hughes and
Galin Jones Bayesian Spatiotemporal Modeling Using
Hierarchical Spatial Priors, with
Applications to Functional Magnetic
Resonance Imaging (with Discussion) . . 1261--1313
Bo Ning and
Subhashis Ghosal and
Jewell Thomas Bayesian Method for Causal Inference in
Spatially-Correlated Multivariate Time
Series . . . . . . . . . . . . . . . . . 1--28
Emilian R. Vankov and
Michele Guindani and
Katherine B. Ensor Filtering and Estimation for a Class of
Stochastic Volatility Models with
Intractable Likelihoods . . . . . . . . 29--52
Chris Glynn and
Surya T. Tokdar and
Brian Howard and
David L. Banks Bayesian Analysis of Dynamic Linear
Topic Models . . . . . . . . . . . . . . 53--80
Robert J. B. Goudie and
Anne M. Presanis and
David Lunn and
Daniela De Angelis and
Lorenz Wernisch Joining and Splitting Models with Markov
Melding . . . . . . . . . . . . . . . . 81--109
Paul-Marie Grollemund and
Christophe Abraham and
Me\"\ili Baragatti and
Pierre Pudlo Bayesian Functional Linear Regression
with Sparse Step Functions . . . . . . . 111--135
Kaoru Irie and
Mike West Bayesian Emulation for Multi-Step
Optimization in Decision Problems . . . 137--160
Jacopo Soriano and
Li Ma Mixture Modeling on Related Samples by $
\psi $-Stick Breaking and Kernel
Perturbation . . . . . . . . . . . . . . 161--180
Matthew J. Keefe and
Marco A. R. Ferreira and
Christopher T. Franck Objective Bayesian Analysis for Gaussian
Hierarchical Models with Intrinsic
Conditional Autoregressive Priors . . . 181--209
Brenda N. Vo and
Christopher C. Drovandi and
Anthony N. Pettitt Bayesian Parametric Bootstrap for Models
with Intractable Likelihoods . . . . . . 211--234
Wenxin Jiang and
Cheng Li On Bayesian Oracle Properties . . . . . 235--260
Dave Osthus and
James Gattiker and
Reid Priedhorsky and
Sara Y. Del Valle Dynamic Bayesian Influenza Forecasting
in the United States with Hierarchical
Discrepancy (with Discussion) . . . . . 261--312
Lloyd T. Elliott and
Maria De Iorio and
Stefano Favaro and
Kaustubh Adhikari and
Yee Whye Teh Modeling Population Structure Under
Hierarchical Dirichlet Processes . . . . 313--339
Daniela Pauger and
Helga Wagner Bayesian Effect Fusion for Categorical
Predictors . . . . . . . . . . . . . . . 341--369
M. W. McLean and
M. P. Wand Variational Message Passing for
Elaborate Response Regression Models . . 371--398
Haolun Shi and
Guosheng Yin Control of Type I Error Rates in
Bayesian Sequential Designs . . . . . . 399--425
Luis Gutiérrez and
Eduardo Gutiérrez-Peña and
Ramsés H. Mena A Bayesian Approach to Statistical Shape
Analysis via the Projected Normal
Distribution . . . . . . . . . . . . . . 427--447
Suprateek Kundu and
Bani K. Mallick and
Veera Baladandayuthapani Efficient Bayesian Regularization for
Graphical Model Selection . . . . . . . 449--476
Lukasz Rajkowski Analysis of the Maximal a Posteriori
Partition in the Gaussian Dirichlet
Process Mixture Model . . . . . . . . . 477--494
Benjamin Letham and
Brian Karrer and
Guilherme Ottoni and
Eytan Bakshy Constrained Bayesian Optimization with
Noisy Experiments . . . . . . . . . . . 495--519
Joris Mulder and
Jean-Paul Fox Bayes Factor Testing of Multiple
Intraclass Correlations . . . . . . . . 521--552
Alberto Cassese and
Weixuan Zhu and
Michele Guindani and
Marina Vannucci A Bayesian Nonparametric Spiked Process
Prior for Dynamic Model Selection . . . 553--572
Quan Zhou and
Yongtao Guan Fast Model-Fitting of Bayesian Variable
Selection Regression Using the Iterative
Complex Factorization Algorithm . . . . 573--594
Marko Järvenpää and
Michael U. Gutmann and
Arijus Pleska and
Aki Vehtari and
Pekka Marttinen Efficient Acquisition Rules for
Model-Based Approximate Bayesian
Computation . . . . . . . . . . . . . . 595--622
Marcos Oliveira Prates and
Renato Martins Assunção and
Erica Castilho Rodrigues Alleviating Spatial Confounding for
Areal Data Problems by Displacing the
Geographical Centroids . . . . . . . . . 623--647
Luis Gutiérrez and
Andrés F. Barrientos and
Jorge González and
Daniel Taylor-Rodríguez A Bayesian Nonparametric Multiple
Testing Procedure for Comparing Several
Treatments Against a Control . . . . . . 649--675
Yushu Shi and
Michael Martens and
Anjishnu Banerjee and
Purushottam Laud Low Information Omnibus (LIO) Priors for
Dirichlet Process Mixture Models . . . . 677--702
Sohan Seth and
Iain Murray and
Christopher K. I. Williams Model Criticism in Latent Space . . . . 703--725
Stefano Peluso and
Siddhartha Chib and
Antonietta Mira Semiparametric Multivariate and Multiple
Change-Point Modeling . . . . . . . . . 727--751
L. F. South and
A. N. Pettitt and
C. C. Drovandi Sequential Monte Carlo Samplers with
Independent Markov Chain Monte Carlo
Proposals . . . . . . . . . . . . . . . 753--776
Ioannis Ntzoufras and
Claudia Tarantola and
Monia Lupparelli Probability Based Independence Sampler
for Bayesian Quantitative Learning in
Graphical Log--Linear Marginal Models 777--803
Joseph Antonelli and
Giovanni Parmigiani and
Francesca Dominici High-Dimensional Confounding Adjustment
Using Continuous Spike and Slab Priors 805--828
Brian Neelon Bayesian Zero-Inflated Negative Binomial
Regression Based on Pólya--Gamma Mixtures 829--855
Mengyang Gu Jointly Robust Prior for Gaussian
Stochastic Process in Emulation,
Calibration and Variable Selection . . . 857--885
Lizhen Lin and
Niu Mu and
Pokman Cheung and
David Dunson Extrinsic Gaussian Processes for
Regression and Classification on
Manifolds . . . . . . . . . . . . . . . 887--906
Muteb Alharthi and
Theodore Kypraios and
Philip D. O'Neill Bayes Factors for Partially Observed
Stochastic Epidemic Models . . . . . . . 907--936
Jon Cockayne and
Chris J. Oates and
Ilse C. F. Ipsen and
Mark Girolami A Bayesian Conjugate Gradient Method
(with Discussion) . . . . . . . . . . . 937--1012
Miguel de Carvalho and
Garritt L. Page and
Bradley J. Barney On the Geometry of Bayesian Inference 1013--1036
Claudia Kirch and
Matthew C. Edwards and
Alexander Meier and
Renate Meyer Beyond Whittle: Nonparametric Correction
of a Parametric Likelihood with a Focus
on Bayesian Time Series Analysis . . . . 1037--1073
Amir Bashir and
Carlos M. Carvalho and
P. Richard Hahn and
M. Beatrix Jones Post-Processing Posteriors Over
Precision Matrices to Produce Sparse
Graph Estimates . . . . . . . . . . . . 1075--1090
Gemma E. Moran and
Veronika Rocková and
Edward I. George Variance Prior Forms for
High-Dimensional Bayesian Variable
Selection . . . . . . . . . . . . . . . 1091--1119
Guillaume Kon Kam King and
Antonio Canale and
Matteo Ruggiero Bayesian Functional Forecasting with
Locally-Autoregressive Dependent
Processes . . . . . . . . . . . . . . . 1121--1141
Nadja Klein and
Michael Stanley Smith Implicit Copulas from Bayesian
Regularized Regression Smoothers . . . . 1143--1171
Lutz F. Gruber and
Erica F. Stuber and
Lyndsie S. Wszola and
Joseph J. Fontaine Estimating the Use of Public Lands:
Integrated Modeling of Open Populations
with Convolution Likelihood Ecological
Abundance Regression . . . . . . . . . . 1173--1199
Julyan Arbel and
Pierpaolo De Blasi and
Igor Prünster Stochastic Approximations to the
Pitman--Yor Process . . . . . . . . . . 1201--1219
Abhirup Datta and
Sudipto Banerjee and
James S. Hodges and
Leiwen Gao Spatial Disease Mapping Using Directed
Acyclic Graph Auto-Regressive (DAGAR)
Models . . . . . . . . . . . . . . . . . 1221--1244
Jeong Eun Lee and
Geoff K. Nicholls and
Robin J. Ryder Calibration Procedures for Approximate
Bayesian Credible Sets . . . . . . . . . 1245--1269
Andrea Cremaschi and
Raffaele Argiento and
Katherine Shoemaker and
Christine Peterson and
Marina Vannucci Hierarchical Normalized Completely
Random Measures for Robust Graphical
Modeling . . . . . . . . . . . . . . . . 1271--1301
Federico Camerlenghi and
David B. Dunson and
Antonio Lijoi and
Igor Prünster and
Abel Rodríguez Latent Nested Nonparametric Priors (with
Discussion) . . . . . . . . . . . . . . 1303--1356
Matthew Moores and
Geoff Nicholls and
Anthony Pettitt and
Kerrie Mengersen Scalable Bayesian Inference for the
Inverse Temperature of a Hidden Potts
Model . . . . . . . . . . . . . . . . . 1--27
Nicolas Garcia Trillos and
Daniel Sanz-Alonso The Bayesian Update: Variational
Formulations and Gradient Flows . . . . 29--56
Matthew R. Williams and
Terrance D. Savitsky Bayesian Estimation Under Informative
Sampling with Unattenuated Dependence 57--77
Qingpo Cai and
Jian Kang and
Tianwei Yu Bayesian Network Marker Selection via
the Thresholded Graph Laplacian Gaussian
Prior . . . . . . . . . . . . . . . . . 79--102
Antony Overstall and
James McGree Bayesian Design of Experiments for
Intractable Likelihood Models Using
Coupled Auxiliary Models and
Multivariate Emulation . . . . . . . . . 103--131
Jong Hee Park and
Yunkyu Sohn Detecting Structural Changes in
Longitudinal Network Data . . . . . . . 133--157
Fangzheng Xie and
Yanxun Xu Adaptive Bayesian Nonparametric
Regression Using a Kernel Mixture of
Polynomials with Application to Partial
Linear Models . . . . . . . . . . . . . 159--186
Ilaria Bianchini and
Alessandra Guglielmi and
Fernando A. Quintana Determinantal Point Process Mixtures Via
Spectral Density Approach . . . . . . . 187--214
Abhishek Bishoyi and
Xiaojing Wang and
Dipak K. Dey Learning Semiparametric Regression with
Missing Covariates Using Gaussian
Process Models . . . . . . . . . . . . . 215--239
Xuan Cao and
Kshitij Khare and
Malay Ghosh High-Dimensional Posterior Consistency
for Hierarchical Non-Local Priors in
Regression . . . . . . . . . . . . . . . 241--262
Aliaksandr Hubin and
Geir Storvik and
Florian Frommlet A Novel Algorithmic Approach to Bayesian
Logic Regression (with Discussion) . . . 263--333
Kelly C. M. Gonçalves and
Hélio S. Migon and
Leonardo S. Bastos Dynamic Quantile Linear Models: A
Bayesian Approach . . . . . . . . . . . 335--362
Andrés F. Barrientos and
Víctor Peña Bayesian Bootstraps for Massive Data . . 363--388
Philippe Gagnon and
Alain Desgagné and
Myl\`ene Bédard A New Bayesian Approach to Robustness
Against Outliers in Linear Regression 389--414
Jarno Vanhatalo and
Marcelo Hartmann and
Lari Veneranta Additive Multivariate Gaussian Processes
for Joint Species Distribution Modeling
with Heterogeneous Data . . . . . . . . 415--447
Jami J. Mulgrave and
Subhashis Ghosal Bayesian Inference in Nonparanormal
Graphical Models . . . . . . . . . . . . 449--475
Rajarshi Guhaniyogi and
Abel Rodriguez Joint Modeling of Longitudinal
Relational Data and Exogenous Variables 477--503
Maxim Rabinovich and
Aaditya Ramdas and
Michael I. Jordan and
Martin J. Wainwright Function-Specific Mixing Times and
Concentration Away from Equilibrium . . 505--532
Cristiano Villa and
Jeong Eun Lee A Loss-Based Prior for Variable
Selection in Linear Regression Methods 533--558
Kurtis Shuler and
Marilou Sison-Mangus and
Juhee Lee Bayesian Sparse Multivariate Regression
with Asymmetric Nonlocal Priors for
Microbiome Data Analysis . . . . . . . . 559--578
Zhi-Qiang Wang and
Nian-Sheng Tang Bayesian Quantile Regression with Mixed
Discrete and Nonignorable Missing
Covariates . . . . . . . . . . . . . . . 579--604
Bohai Zhang and
Noel Cressie Bayesian Inference of Spatio-Temporal
Changes of Arctic Sea Ice . . . . . . . 605--631
Andrea Tancredi and
Rebecca Steorts and
Brunero Liseo A Unified Framework for De-Duplication
and Population Size Estimation (with
Discussion) . . . . . . . . . . . . . . 633--682
Creighton Heaukulani and
Daniel M. Roy Gibbs-type Indian Buffet Processes . . . 683--710
Weihong Huang and
Yan Liu and
Yuguo Chen Mixed Membership Stochastic Blockmodels
for Heterogeneous Networks . . . . . . . 711--736
Kyoungjae Lee and
Lizhen Lin Bayesian Bandwidth Test and Selection
for High-dimensional Banded Precision
Matrices . . . . . . . . . . . . . . . . 737--758
Kumaresh Dhara and
Stuart Lipsitz and
Debdeep Pati and
Debajyoti Sinha A New Bayesian Single Index Model with
or without Covariates Missing at Random 759--780
Zehang Richard Li and
Tyler H. McComick and
Samuel J. Clark Using Bayesian Latent Gaussian Graphical
Models to Infer Symptom Associations in
Verbal Autopsies . . . . . . . . . . . . 781--807
Federico Bassetti and
Roberto Casarin and
Luca Rossini Hierarchical Species Sampling Models . . 809--838
Trevelyan J. McKinley and
Peter Neal and
Simon E. F. Spencer and
Andrew J. K. Conlan and
Laurence Tiley Efficient Bayesian Model Choice for
Partially Observed Processes: With
Application to an Experimental
Transmission Study of an Infectious
Disease . . . . . . . . . . . . . . . . 839--870
Subhadip Pal and
Subhajit Sengupta and
Riten Mitra and
Arunava Banerjee Conjugate Priors and Posterior Inference
for the Matrix Langevin Distribution on
the Stiefel Manifold . . . . . . . . . . 871--908
Xinming Yang and
Naveen N. Narisetty Consistent Group Selection with Bayesian
High Dimensional Modeling . . . . . . . 909--935
Keefe Murphy and
Cinzia Viroli and
Isobel Claire Gormley Infinite Mixtures of Infinite Factor
Analysers . . . . . . . . . . . . . . . 937--963
P. Richard Hahn and
Jared S. Murray and
Carlos M. Carvalho Bayesian Regression Tree Models for
Causal Inference: Regularization,
Confounding, and Heterogeneous Effects
(with Discussion) . . . . . . . . . . . 965--1056
Junyang Wang and
Jon Cockayne and
Chris. J. Oates A Role for Symmetry in the Bayesian
Solution of Differential Equations . . . 1057--1085
Akihiko Nishimura and
David Dunson Recycling Intermediate Steps to Improve
Hamiltonian Monte Carlo . . . . . . . . 1087--1108
Geir-Arne Fuglstad and
Ingeborg Gullikstad Hem and
Alexander Knight and
Håvard Rue and
Andrea Riebler Intuitive Joint Priors for Variance
Parameters . . . . . . . . . . . . . . . 1109--1137
Camille M. Moore and
Nichole E. Carlson and
Samantha MaWhinney and
Sarah Kreidler A Dirichlet Process Mixture Model for
Non-Ignorable Dropout . . . . . . . . . 1139--1167
Ali Foroughi pour and
Lori A. Dalton Theory of Optimal Bayesian Feature
Filtering . . . . . . . . . . . . . . . 1169--1197
Shiwei Lan and
Andrew Holbrook and
Gabriel A. Elias and
Norbert J. Fortin and
Hernando Ombao and
Babak Shahbaba Flexible Bayesian Dynamic Modeling of
Correlation and Covariance Matrices . . 1199--1228
Arkaprava Roy and
Subhashis Ghosal and
Kingshuk Roy Choudhury High Dimensional Single-Index Bayesian
Modeling of Brain Atrophy . . . . . . . 1229--1249
Tommi Perälä and
Jarno Vanhatalo and
Anna Chrysafi Calibrating Expert Assessments Using
Hierarchical Gaussian Process Models . . 1251--1280
V\'ìctor Peña and
James O. Berger Restricted Type II Maximum Likelihood
Priors on Regression Coefficients . . . 1281--1297
David R. Bickel An Explanatory Rationale for Priors
Sharpened Into Occam's Razors . . . . . 1299--1321
Dao Nguyen and
Perry de Valpine and
Yves Atchade and
Daniel Turek and
Nicholas Michaud and
Christopher Paciorek Nested Adaptation of MCMC Algorithms . . 1323--1343
Fabrizio Leisen and
Cristiano Villa and
Stephen G. Walker On a Class of Objective Priors from
Scoring Rules (with Discussion) . . . . 1345--1423
F. O. Bunnin and
J. Q. Smith A Bayesian Hierarchical Model for
Criminal Investigations . . . . . . . . 1--30
Fabrizio Ruggeri and
Marta Sánchez-Sánchez and
Miguel Ángel Sordo and
Alfonso Suárez-Llorens On a New Class of Multivariate Prior
Distributions: Theory and Application in
Reliability . . . . . . . . . . . . . . 31--60
Laura C. Dawkins and
Daniel B. Williamson and
Kerrie L. Mengersen and
Lidia Morawska and
Rohan Jayaratne and
Gavin Shaddick Where Is the Clean Air? A Bayesian
Decision Framework for Personalised
Cyclist Route Selection Using R-INLA . . 61--91
Amir Nikooienejad and
Valen E. Johnson On the Existence of Uniformly Most
Powerful Bayesian Tests With Application
to Non-Central Chi-Squared Tests . . . . 93--109
Sean Chang and
James O. Berger Comparison of Bayesian and Frequentist
Multiplicity Correction for Testing
Mutually Exclusive Hypotheses Under Data
Dependence . . . . . . . . . . . . . . . 111--128
Daojiang He and
Dongchu Sun and
Lei He Objective Bayesian Analysis for the
Student-$t$ Linear Regression . . . . . 129--145
Marko Järvenpää and
Michael U. Gutmann and
Aki Vehtari and
Pekka Marttinen Parallel Gaussian Process Surrogate
Bayesian Inference with Noisy Likelihood
Evaluations . . . . . . . . . . . . . . 147--178
Ruitao Lin and
Peter F. Thall and
Ying Yuan A Phase I-II Basket Trial Design to
Optimize Dose-Schedule Regimes Based on
Delayed Outcomes . . . . . . . . . . . . 179--202
David J. Nott and
Max Seah and
Luai Al-Labadi and
Michael Evans and
Hui Khoon Ng and
Berthold-Georg Englert Using Prior Expansions for Prior-Data
Conflict Checking . . . . . . . . . . . 203--231
Veronika Rockova and
Kenichiro McAlinn Dynamic Variable Selection with
Spike-and-Slab Process Priors . . . . . 233--269
María Eugenia Castellanos A Model Selection Approach for Variable
Selection with Censored Data . . . . . . 271--300
Sally Paganin and
Amy H. Herring and
Andrew F. Olshan and
David B. Dunson Centered Partition Processes:
Informative Priors for Clustering (with
Discussion) . . . . . . . . . . . . . . 301--370
Filippo Ascolani and
Antonio Lijoi and
Matteo Ruggiero Predictive inference with
Fleming--Viot-driven dependent Dirichlet
processes . . . . . . . . . . . . . . . 371--395
Umberto Simola and
Jessi Cisewski-Kehe and
Michael U. Gutmann and
Jukka Corander Adaptive Approximate Bayesian
Computation Tolerance Selection . . . . 397--423
Andreas Heinecke and
Lifeng Ye and
Maria De Iorio and
Timothy Ebbels Bayesian Deconvolution and
Quantification of Metabolites from
$J$-Resolved NMR Spectroscopy . . . . . 425--458
Daniel R. Kowal Dynamic Regression Models for
Time-Ordered Functional Data . . . . . . 459--487
Samantha Leorato and
Maura Mezzetti A Bayesian Factor Model for Spatial
Panel Data with a Separable Covariance
Approach . . . . . . . . . . . . . . . . 489--519
Lu Shaochuan Bayesian Multiple Changepoint Detection
for Stochastic Models in Continuous Time 521--544
Nadja Klein and
Manuel Carlan and
Thomas Kneib and
Stefan Lang and
Helga Wagner Bayesian Effect Selection in Structured
Additive Distributional Regression
Models . . . . . . . . . . . . . . . . . 545--573
Imke Botha and
Robert Kohn and
Christopher Drovandi Particle Methods for Stochastic
Differential Equation Mixed Effects
Models . . . . . . . . . . . . . . . . . 575--609
Birgir Hrafnkelsson and
Stefan Siegert and
Raphaël Huser and
Haakon Bakka and
Árni V. Jóhannesson Max-and-Smooth: a Two-Step Approach for
Approximate Bayesian Inference in Latent
Gaussian Models . . . . . . . . . . . . 611--638
Arya A. Pourzanjani and
Richard M. Jiang and
Brian Mitchell and
Paul J. Atzberger and
Linda R. Petzold Bayesian Inference over the Stiefel
Manifold via the Givens Representation 639--666
Aki Vehtari and
Andrew Gelman and
Daniel Simpson and
Bob Carpenter and
Paul-Christian Bürkner Rank-Normalization, Folding, and
Localization: an Improved . . . . . . . 667--718
Yuxiang Gao and
Lauren Kennedy and
Daniel Simpson and
Andrew Gelman Improving Multilevel Regression and
Poststratification with Structured
Priors . . . . . . . . . . . . . . . . . 719--744
Alexander Buchholz and
Nicolas Chopin and
Pierre E. Jacob Adaptive Tuning of Hamiltonian Monte
Carlo Within Sequential Monte Carlo . . 745--771
Steven Kleinegesse and
Christopher Drovandi and
Michael U. Gutmann Sequential Bayesian Experimental Design
for Implicit Models via Mutual
Information . . . . . . . . . . . . . . 773--802
Audrey Béliveau and
Paul Gustafson A Theoretical Investigation of How
Evidence Flows in Bayesian Network
Meta-Analysis of Disconnected Networks 803--823
Thomas A. Murray and
Peter F. Thall and
Frederique Schortgen and
Pierre Asfar and
Sarah Zohar and
Sandrine Katsahian Robust Adaptive Incorporation of
Historical Control Data in a Randomized
Trial of External Cooling to Treat
Septic Shock . . . . . . . . . . . . . . 825--844
Emilio Porcu and
Pier Giovanni Bissiri and
Felipe Tagle and
Rubén Soza and
Fernando A. Quintana Nonparametric Bayesian Modeling and
Estimation of Spatial Correlation
Functions for Global Data . . . . . . . 845--873
Teng Wu and
Naveen N. Narisetty Bayesian Multiple Quantile Regression
for Linear Models Using a Score
Likelihood . . . . . . . . . . . . . . . 875--903
Alan Benson and
Nial Friel Bayesian Inference, Model Selection and
Likelihood Estimation using Fast
Rejection Sampling: The
Conway--Maxwell--Poisson Distribution 905--931
Tim van Erven and
Botond Szabó Fast Exact Bayesian Inference for Sparse
Signals in the Normal Sequence Model . . 933--960
Allard Hendriksen and
Rianne de Heide and
Peter Grünwald Optional Stopping with Bayes Factors: a
Categorization and Extension of Folklore
Results, with an Application to
Invariant Situations . . . . . . . . . . 961--989
Lane F. Burgette and
David Puelz and
P. Richard Hahn A Symmetric Prior for Multinomial Probit
Models . . . . . . . . . . . . . . . . . 991--1008
Miguel González and
Carmen Minuesa and
Inés del Puerto and
Anand N. Vidyashankar Robust Estimation in Controlled
Branching Processes: Bayesian Estimators
via Disparities . . . . . . . . . . . . 1009--1037
Riddhi Pratim Ghosh and
Bani Mallick and
Mohsen Pourahmadi Bayesian Estimation of Correlation
Matrices of Longitudinal Data . . . . . 1039--1058
Isaac Lavine and
Michael Lindon and
Mike West Adaptive Variable Selection for
Sequential Prediction in Multivariate
Dynamic Models . . . . . . . . . . . . . 1059--1083
Maria M. Barbieri and
James O. Berger and
Edward I. George and
Veronika Rocková The Median Probability Model and
Correlated Variables . . . . . . . . . . 1085--1112
Federico Castelletti and
Guido Consonni Bayesian Causal Inference in Probit
Graphical Models . . . . . . . . . . . . 1113--1137
Edward George and
Gourab Mukherjee and
Keisuke Yano Optimal Shrinkage Estimation of
Predictive Densities Under $ \alpha
$-Divergences . . . . . . . . . . . . . 1139--1155
Sayar Karmakar and
Arkaprava Roy Bayesian Modelling of Time-Varying
Conditional Heteroscedasticity . . . . . 1157--1185
Mario Beraha and
Alessandra Guglielmi and
Fernando A. Quintana The Semi-Hierarchical Dirichlet Process
and Its Application to Clustering
Homogeneous Distributions . . . . . . . 1187--1219
Rajarshi Guhaniyogi and
Daniel Spencer Bayesian Tensor Response Regression with
an Application to Brain Activation
Studies . . . . . . . . . . . . . . . . 1221--1249
Per Sidén and
Finn Lindgren and
David Bolin and
Anders Eklund and
Mattias Villani Spatial $3$D Matérn Priors for Fast
Whole-Brain fMRI Analysis . . . . . . . 1251--1278
Sylvia Frühwirth-Schnatter and
Gertraud Malsiner-Walli and
Bettina Grün Generalized Mixtures of Finite Mixtures
and Telescoping Sampling . . . . . . . . 1279--1307
Giacomo Zanella and
Gareth Roberts Multilevel Linear Models, Gibbs Samplers
and Multigrid Decompositions (with
Discussion) . . . . . . . . . . . . . . 1309--1391
John R. Lewis and
Steven N. MacEachern and
Yoonkyung Lee Bayesian Restricted Likelihood Methods:
Conditioning on Insufficient Statistics
in Bayesian Regression (with Discussion) 1393--1462
Owen Thomas and
Ritabrata Dutta and
Jukka Corander and
Samuel Kaski and
Michael U. Gutmann Likelihood-Free Inference by Ratio
Estimation . . . . . . . . . . . . . . . 1--31
Alejandra Avalos-Pacheco and
David Rossell and
Richard S. Savage Heterogeneous Large Datasets Integration
Using Bayesian Factor Regression . . . . 33--66
Marie-Pier Côté and
Christian Genest and
David A. Stephens A Bayesian Approach to Modeling
Multivariate Multilevel Insurance Claims
in the Presence of Unsettled Claims . . 67--93
Guilherme Lopes de Oliveira and
Raffaele Argiento and
Rosangela Helena Loschi and
Renato Martins Assunção and
Fabrizio Ruggeri and
Márcia D'Elia Branco Bias Correction in Clustered
Underreported Data . . . . . . . . . . . 95--126
Jonathan R. Bradley Joint Bayesian Analysis of Multiple
Response-Types Using the Hierarchical
Generalized Transformation Model . . . . 127--164
Louis Raynal and
Sixing Chen and
Antonietta Mira and
Jukka-Pekka Onnela Scalable Approximate Bayesian
Computation for Growing Network Models
via Extrapolated and Sampled Summaries 165--192
Evgeny Levi and
Radu V. Craiu Finding our Way in the Dark: Approximate
MCMC for Approximate Bayesian Methods 193--221
Christopher Drovandi and
Richard G. Everitt and
Andrew Golightly and
Dennis Prangle Ensemble MCMC: Accelerating
Pseudo-Marginal MCMC for State Space
Models using the Ensemble Kalman Filter 223--260
Bruno Buonaguidi and
Antonietta Mira and
Herbert Bucheli and
Viton Vitanis Bayesian Quickest Detection of Credit
Card Fraud . . . . . . . . . . . . . . . 261--290
Brian Kidd and
Matthias Katzfuss Bayesian Nonstationary and Nonparametric
Covariance Estimation for Large Spatial
Data (with Discussion) . . . . . . . . . 291--351
Ben Lambert and
Aki Vehtari $ R^\ast $: a Robust MCMC Convergence
Diagnostic with Uncertainty Using
Decision Tree Classifiers . . . . . . . 353--379
Marcos A. Capistrán and
J. Andrés Christen and
María L. Daza-Torres and
Hugo Flores-Arguedas and
J. Cricelio Montesinos-López Error Control of the Numerical Posterior
with Bayes Factors in Bayesian
Uncertainty Quantification . . . . . . . 381--403
Sara Wade and
Raffaella Piccarreta and
Andrea Cremaschi and
Isadora Antoniano-Villalobos Colombian Women's Life Patterns: a
Multivariate Density Regression Approach 405--433
Samuel I. Berchuck and
Mark Janko and
Felipe A. Medeiros and
William Pan and
Sayan Mukherjee Bayesian Non-Parametric Factor Analysis
for Longitudinal Spatial Surfaces . . . 435--464
Stephen R. Johnson and
Daniel A. Henderson and
Richard J. Boys On Bayesian inference for the Extended
Plackett--Luce model . . . . . . . . . . 465--490
Ilsang Ohn and
Yongdai Kim Posterior Consistency of Factor
Dimensionality in High-Dimensional
Sparse Factor Models . . . . . . . . . . 491--514
Hugh A. Chipman and
Edward I. George and
Robert E. McCulloch and
Thomas S. Shively mBART: Multidimensional Monotone BART 515--544
Yasuyuki Hamura and
Kaoru Irie and
Shonosuke Sugasawa On Global-Local Shrinkage Priors for
Count Data . . . . . . . . . . . . . . . 545--564
David Rossell Concentration of Posterior Model
Probabilities and Normalized . . . . . . 565--591
David J. Warne and
Scott A. Sisson and
Christopher Drovandi Vector Operations for Accelerating
Expensive Bayesian Computations --- A
Tutorial Guide . . . . . . . . . . . . . 593--622
Max Goplerud Fast and Accurate Estimation of
Non-Nested Binomial Hierarchical Models
Using Variational Inference . . . . . . 623--650
Angelos Alexopoulos and
Petros Dellaportas and
Omiros Papaspiliopoulos Bayesian Prediction of Jumps in Large
Panels of Time Series Data . . . . . . . 651--683
Sergio Bacallado and
Stefano Favaro and
Samuel Power and
Lorenzo Trippa Perfect Sampling of the Posterior in the
Hierarchical Pitman--Yor Process . . . . 685--709
Vasileios Maroulas and
Cassie Putman Micucci and
Farzana Nasrin Bayesian Topological Learning for
Classifying the Structure of Biological
Networks . . . . . . . . . . . . . . . . 711--736
David E. Jones and
Robert N. Trangucci and
Yang Chen Quantifying Observed Prior Impact . . . 737--764
Daniel Ayala and
Leonardo Jofré and
Luis Gutiérrez and
Ramsés H. Mena On a Dirichlet Process Mixture
Representation of Phase-Type
Distributions . . . . . . . . . . . . . 765--790
Terrance D. Savitsky and
Matthew R. Williams Bayesian Dependent Functional Mixture
Estimation for Area and Time-Indexed
Data: an Application for the Prediction
of Monthly County Employment . . . . . . 791--815
Wei Shi and
Ming-Hui Chen and
Lynn Kuo and
Paul O. Lewis Bayesian Concentration Ratio and
Dissonance . . . . . . . . . . . . . . . 817--847
William Hua and
Hongyuan Mei and
Sarah Zohar and
Magali Giral and
Yanxun Xu Personalized Dynamic Treatment Regimes
in Continuous Time: a Bayesian Approach
for Optimizing Clinical Decisions with
Timing . . . . . . . . . . . . . . . . . 849--878
Jin Wang and
Yunbo Ouyang and
Yuan Ji and
Feng Liang An Ensemble EM Algorithm for Bayesian
Variable Selection . . . . . . . . . . . 879--900
Yucong Ma and
Jun S. Liu On Posterior Consistency of Bayesian
Factor Models in High Dimensions . . . . 901--929
Richard L. Warr and
David B. Dahl and
Jeremy M. Meyer and
Arthur Lui The Attraction Indian Buffet
Distribution . . . . . . . . . . . . . . 931--967
Alejandro Murua and
Fernando Andrés Quintana Biclustering via Semiparametric Bayesian
Inference . . . . . . . . . . . . . . . 969--995
Antonio R. Linero and
Piyali Basak and
Yinpu Li and
Debajyoti Sinha Bayesian Survival Tree Ensembles with
Submodel Shrinkage . . . . . . . . . . . 997--1020
Antony Overstall and
James McGree Bayesian Decision-Theoretic Design of
Experiments Under an Alternative Model 1021--1041
Yuling Yao and
Gregor Pirs and
Aki Vehtari and
Andrew Gelman Bayesian Hierarchical Stacking: Some
Models Are (Somewhere) Useful . . . . . 1043--1071
Dimitris Fouskakis and
Ioannis Ntzoufras Power-Expected-Posterior Priors as
Mixtures of $g$-Priors in Normal Linear
Models . . . . . . . . . . . . . . . . . 1073--1099
Yaozhong Hu and
Junxi Zhang Functional Central Limit Theorems for
Stick-Breaking Priors . . . . . . . . . 1101--1120
Tianyu Cui and
Aki Havulinna and
Pekka Marttinen and
Samuel Kaski Informative Bayesian Neural Network
Priors for Weak Signals . . . . . . . . 1121--1151
Fan Yin and
Weining Shen and
Carter T. Butts Finite Mixtures of ERGMs for Modeling
Ensembles of Networks . . . . . . . . . 1153--1191
Fangzheng Xie and
Joshua Cape and
Carey E. Priebe and
Yanxun Xu Bayesian Sparse Spiked Covariance Model
with a Continuous Matrix Shrinkage Prior 1193--1217
Mengyang Gu and
Hanmo Li Gaussian Orthogonal Latent Factor
Processes for Large Incomplete Matrices
of Correlated Data . . . . . . . . . . . 1219--1244
Matthew Heiner and
Athanasios Kottas Bayesian Nonparametric Density
Autoregression with Lag Selection . . . 1245--1273
Sharmistha Guha and
Jerome P. Reiter and
Andrea Mercatanti Bayesian Causal Inference with Bipartite
Record Linkage . . . . . . . . . . . . . 1275--1299
Sébastien Marmin and
Maurizio Filippone Deep Gaussian Processes for Calibration
of Computer Models (with Discussion) . . 1301--1350
Nathan Sandholtz and
Yohsuke Miyamoto and
Luke Bornn and
Maurice A. Smith Inverse Bayesian Optimization: Learning
Human Acquisition Functions in an
Exploration vs Exploitation Search Task 1--24
Kyoungjae Lee and
Lizhen Lin Scalable Bayesian High-dimensional Local
Dependence Learning . . . . . . . . . . 25--47
Joshua Daniel Loyal and
Yuguo Chen A Bayesian Nonparametric Latent Space
Approach to Modeling Evolving
Communities in Dynamic Networks . . . . 49--77
Jonathan H. Huggins and
Jeffrey W. Miller Reproducible Model Selection Using
Bagged Posteriors . . . . . . . . . . . 79--104
Pei-Shien Wu and
Ryan Martin A Comparison of Learning Rate Selection
Methods in Generalized Bayesian
Inference . . . . . . . . . . . . . . . 105--132
Dennis Prangle and
Sophie Harbisher and
Colin S. Gillespie Bayesian Experimental Design Without
Posterior Calculations: an Adversarial
Approach . . . . . . . . . . . . . . . . 133--163
Samuel E. Jackson and
Ian Vernon Efficient Emulation of Computer Models
Utilising Multiple Known Boundaries of
Differing Dimension . . . . . . . . . . 165--191
Olli Saarela and
Christian Rohrbeck and
Elja Arjas Bayesian Non-Parametric Ordinal
Regression Under a Monotonicity
Constraint . . . . . . . . . . . . . . . 193--221
Luiz M. Carvalho and
Daniel A. M. Villela and
Flavio C. Coelho and
Leonardo S. Bastos Bayesian Inference for the Weights in
Logarithmic Pooling . . . . . . . . . . 223--251
Riccardo Passeggeri On Quasi-Infinitely Divisible Random
Measures . . . . . . . . . . . . . . . . 253--286
Ryan Giordano and
Runjing Liu and
Michael I. Jordan and
Tamara Broderick Evaluating Sensitivity to the
Stick-Breaking Prior in Bayesian
Nonparametrics (with Discussion) . . . . 287--366
Akihiko Nishimura and
Marc A. Suchard Shrinkage with Shrunken Shoulders: Gibbs
Sampling Shrinkage Model Posteriors with
Guaranteed Convergence Rates . . . . . . 367--390
Marta Crispino and
Isadora Antoniano-Villalobos Informative Priors for the Consensus
Ranking in the Bayesian Mallows Model 391--414
Brandon Berman and
Wesley O. Johnson and
Weining Shen Normal Approximation for Bayesian Mixed
Effects Binomial Regression Models . . . 415--435
Wael A. J. Al-Taie and
Malcolm Farrow Bayes Linear Bayes Networks with an
Application to Prognostic Indices . . . 437--463
Eunice Okome Obiang and
Pascal Jézéquel and
Frédéric Pro\"\ia A Bayesian Approach for Partial Gaussian
Graphical Models With Sparsity . . . . . 465--490
Henry Shaowu Yuchi and
Simon Mak and
Yao Xie Bayesian Uncertainty Quantification for
Low-Rank Matrix Completion . . . . . . . 491--518
Andrew Chapple and
Yussef Bennani and
Meredith Clement A Multi-Armed Bayesian Ordinal Outcome
Utility-Based Sequential Trial with a
Pairwise Null Clustering Prior . . . . . 519--546
Beniamino Hadj-Amar and
Jack Jewson and
Mark Fiecas Bayesian Approximations to Hidden
Semi-Markov Models for Telemetric
Monitoring of Physical Activity . . . . 547--577
Guanyu Hu and
Junxian Geng and
Yishu Xue and
Huiyan Sang Bayesian Spatial Homogeneity Pursuit of
Functional Data: An Application to the
U.S. Income Distribution . . . . . . . . 579--605
Alexander Buchholz and
Daniel Ahfock and
Sylvia Richardson Distributed Computation for Marginal
Likelihood based Model Choice . . . . . 607--638
David A. Stephens and
Widemberg S. Nobre and
Erica E. M. Moodie and
Alexandra M. Schmidt Causal Inference Under
Mis-Specification: Adjustment Based on
the Propensity Score (with Discussion) 639--694
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Editorial Board . . . . . . . . . . . . ??
Xi Chen and
Farhan Feroz and
Michael Hobson Bayesian Posterior Repartitioning for
Nested Sampling . . . . . . . . . . . . 695--721
Hunanyan Sona and
Rue Håvard and
Plummer Martyn and
Roos Ma\lgorzata Quantification of Empirical Determinacy:
The Impact of Likelihood Weighting on
Posterior Location and Spread in
Bayesian Meta-Analysis Estimated with
JAGS and INLA . . . . . . . . . . . . . 723--751
Andrea Cremaschi and
Raffaele Argiento and
Maria De Iorio and
Cai Shirong and
Yap Seng Chong and
Michael Meaney and
Michelle Kee Seemingly Unrelated Multi-State
Processes: a Bayesian Semiparametric
Approach . . . . . . . . . . . . . . . . 753--775
Andrés F. Barrientos and
Deborshee Sen and
Garritt L. Page and
David B. Dunson Bayesian Inferences on Uncertain Ranks
and Orderings: Application to Ranking
Players and Lineups . . . . . . . . . . 777--806
Andrew A. Manderson and
Robert J. B. Goudie Combining Chains of Bayesian Models with
Markov Melding . . . . . . . . . . . . . 807--840
Philippe Gagnon Robustness Against Conflicting Prior
Information in Regression . . . . . . . 841--864
L. F. South and
C. J. Oates and
A. Mira and
C. Drovandi Regularized Zero-Variance Control
Variates . . . . . . . . . . . . . . . . 865--888
Philip Greengard and
Jeremy Hoskins and
Charles C. Margossian and
Jonah Gabry and
Andrew Gelman and
Aki Vehtari Fast Methods for Posterior Inference of
Two-Group Normal-Normal Models . . . . . 889--907
Matthias Sachs and
Deborshee Sen and
Jianfeng Lu and
David Dunson Posterior Computation with the Gibbs
Zig-Zag Sampler . . . . . . . . . . . . 909--927
Michele Zemplenyi and
Jeffrey W. Miller Bayesian Optimal Experimental Design for
Inferring Causal Structure . . . . . . . 929--956
Mélodie Monod and
Alexandra Blenkinsop and
Andrea Brizzi and
Yu Chen and
Carlos Cardoso Correia Perello and
Vidoushee Jogarah and
Yuanrong Wang and
Seth Flaxman and
Samir Bhatt and
Oliver Ratmann Regularised B-splines Projected Gaussian
Process Priors to Estimate Time-trends
in Age-specific COVID-19 Deaths . . . . 957--987
Matias Quiroz and
David J. Nott and
Robert Kohn Gaussian Variational Approximations for
High-dimensional State Space Models . . 989--1016
Kwangmin Lee and
Kyoungjae Lee and
Jaeyong Lee Post-Processed Posteriors for Banded
Covariances . . . . . . . . . . . . . . 1017--1040
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Editorial Board . . . . . . . . . . . . ??
Yuexi Wang and
Nicholas Polson and
Vadim O. Sokolov Data Augmentation for Bayesian Deep
Learning . . . . . . . . . . . . . . . . 1041--1069
Gemma E. Moran and
John P. Cunningham and
David M. Blei The Posterior Predictive Null . . . . . 1071--1097
Umberto Picchini and
Umberto Simola and
Jukka Corander Sequentially Guided MCMC Proposals for
Synthetic Likelihoods and Correlated
Synthetic Likelihoods . . . . . . . . . 1099--1129
Sharmistha Guha and
Abel Rodriguez High-Dimensional Bayesian Network
Classification with Network Global-Local
Shrinkage Priors . . . . . . . . . . . . 1131--1160
Daniel R. Kowal and
Antonio Canale Semiparametric Functional Factor Models
with Bayesian Rank Selection . . . . . . 1161--1189
Xiaotian Zheng and
Athanasios Kottas and
Bruno Sansó Nearest-Neighbor Mixture Models for
Non-Gaussian Spatial Processes . . . . . 1191--1222
Rafael Cabral and
David Bolin and
Håvard Rue Controlling the Flexibility of
Non-Gaussian Processes Through Shrinkage
Priors . . . . . . . . . . . . . . . . . 1223--1246
Danna L. Cruz-Reyes and
Renato M. Assunção and
Rosangela H. Loschi Inducing High Spatial Correlation with
Randomly Edge-Weighted Neighborhood
Graphs . . . . . . . . . . . . . . . . . 1247--1281
Georgios Aristotelous and
Theodore Kypraios and
Philip D. O'Neill Posterior Predictive Checking for
Partially Observed Stochastic Epidemic
Models . . . . . . . . . . . . . . . . . 1283--1310
Willem van den Boom and
Maria De Iorio and
Alexandros Beskos Bayesian Learning of Graph Substructures 1311--1339
Hongmei Zhang and
Xianzheng Huang and
Hasan Arshad Comparing Dependent Undirected Gaussian
Networks . . . . . . . . . . . . . . . . 1341--1366
Ian Vernon and
John Paul Gosling A Bayesian Computer Model Analysis of
Robust Bayesian Analyses . . . . . . . . 1367--1399
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Editorial Board . . . . . . . . . . . . ??
Asael Fabian Martínez Bayesian Estimation of Topological
Features of Persistence Diagrams . . . . 1--20
José J. Quinlan and
Garritt L. Page and
Luis M. Castro Joint Random Partition Models for
Multivariate Change Point Analysis . . . 21--48
Giorgio Paulon and
Peter Müller and
Victor G. Sal y Rosas Bayesian Nonparametric Bivariate
Survival Regression for Current Status
Data . . . . . . . . . . . . . . . . . . 49--75
Yasuyuki Hamura and
Takahiro Onizuka and
Shintaro Hashimoto and
Shonosuke Sugasawa Sparse Bayesian Inference on
Gamma-Distributed Observations Using
Shape-Scale Inverse-Gamma Mixtures . . . 77--97
Xuan Cao and
Kyoungjae Lee Bayesian Inference on Hierarchical
Nonlocal Priors in Generalized Linear
Models . . . . . . . . . . . . . . . . . 99--122
Fadhel Ayed and
Juho Lee and
François Caron The Normal-Generalised Gamma-Pareto
Process: A Novel Pure-Jump Lévy Process
with Flexible Tail and Jump-Activity
Properties . . . . . . . . . . . . . . . 123--152
Chetkar Jha and
Dongchu Sun A General Scheme for Deriving
Conditional Reference Priors . . . . . . 153--179
Henrique Bolfarine and
Carlos M. Carvalho and
Hedibert F. Lopes and
Jared S. Murray Decoupling Shrinkage and Selection in
Gaussian Linear Factor Analysis . . . . 181--203
Yuki Ohnishi and
Arman Sabbaghi A Bayesian Analysis of Two-Stage
Randomized Experiments in the Presence
of Interference, Treatment Nonadherence,
and Missing Outcomes . . . . . . . . . . 205--234
Zijian Zeng and
Meng Li and
Marina Vannucci Bayesian Image-on-Scalar Regression with
a Spatial Global-Local Spike-and-Slab
Prior . . . . . . . . . . . . . . . . . 235--260
Christian Staerk and
Maria Kateri and
Ioannis Ntzoufras A Metropolized Adaptive Subspace
Algorithm for High-Dimensional Bayesian
Variable Selection . . . . . . . . . . . 261--291
Dennis Christensen Inference for Bayesian Nonparametric
Models with Binary Response Data via
Permutation Counting . . . . . . . . . . 293--318
Arash Amini and
Marina Paez and
Lizhen Lin Hierarchical Stochastic Block Model for
Community Detection in Multiplex
Networks . . . . . . . . . . . . . . . . 319--345
Anonymous Table of Contents . . . . . . . . . . . ??
Anonymous Editorial Board . . . . . . . . . . . . ??
Federico Camerlenghi and
Riccardo Corradin and
Andrea Ongaro Contaminated Gibbs-Type Priors . . . . . 347--376
Roberto Ascari and
Agnese Maria Di Brisco and
Sonia Migliorati and
Andrea Ongaro A Multivariate Mixture Regression Model
for Constrained Responses . . . . . . . 377--405
Hyun Bin Kang and
Yeo Jin Jung and
Jaewoo Park Fast Bayesian Functional Regression for
Non-Gaussian Spatial Data . . . . . . . 407--438
Shai Gorsky and
Cliburn Chan and
Li Ma Coarsened Mixtures of Hierarchical Skew
Normal Kernels for Flow and Mass
Cytometry Analyses . . . . . . . . . . . 439--463
Yang Liu and
Robert J. B. Goudie Generalized Geographically Weighted
Regression Model within a Modularized
Bayesian Framework . . . . . . . . . . . 465--500
Ioannis Papageorgiou and
Ioannis Kontoyiannis Posterior Representations for Bayesian
Context Trees: Sampling, Estimation and
Convergence . . . . . . . . . . . . . . 501--529
Olha Bodnar and
Taras Bodnar Objective Bayesian Meta-Analysis Based
on Generalized Marginal Multivariate
Random Effects Model . . . . . . . . . . 531--564
Andrew Magee and
Michael Karcher and
Frederick A. Matsen IV and
Volodymyr M. Minin How Trustworthy Is Your Tree? Bayesian
Phylogenetic Effective Sample Size
Through the Lens of Monte Carlo Error 565--593
Qian Zhang and
Faming Liang Bayesian Analysis of Exponential Random
Graph Models Using Stochastic Gradient
Markov Chain Monte Carlo . . . . . . . . 595--621
Maria Masotti and
Lin Zhang and
Gregory J. Metzger and
Joseph S. Koopmeiners A General Bayesian Functional Spatial
Partitioning Method for Multiple Region
Discovery Applied to Prostate Cancer MRI 623--647
Karl L. Hallgren and
Nicholas A. Heard and
Melissa J. M. Turcotte Changepoint Detection on a Graph of Time
Series . . . . . . . . . . . . . . . . . 649--676
Hugo L. Hammer and
Michael A. Riegler and
Håkon Tjelmeland Approximate Bayesian Inference Based on
Expected Evaluation . . . . . . . . . . 677--698
Fabian Dablander and
Don van den Bergh and
Eric-Jan Wagenmakers and
Alexander Ly Default Bayes Factors for Testing the
(In)equality of Several Population
Variances . . . . . . . . . . . . . . . 699--723
Marco Gramatica and
Silvia Liverani and
Peter Congdon Structure Induced by a Multiple
Membership Transformation on the
Conditional Autoregressive Model . . . . 725--749
Anna Pajor and
Jacek Osiewalski and
Justyna Wróblewska and
Lukasz Kwiatkowski Bayesian ex Post Evaluation of Recursive
Multi-Step-Ahead Density Prediction . . 751--783
Jonathan Boss and
Jyotishka Datta and
Xin Wang and
Sung Kyun Park and
Jian Kang and
Bhramar Mukherjee Group Inverse-Gamma Gamma Shrinkage for
Sparse Linear Models with
Block-Correlated Regressors . . . . . . 785--814
Chirag Modi and
Alex Barnett and
Bob Carpenter Delayed rejection Hamiltonian Monte
Carlo for sampling multiscale
distributions . . . . . . . . . . . . . 815--842
Saverio Ranciati and
Veronica Vinciotti and
Ernst C. Wit and
Giuliano Galimberti Mixtures of Probit Regression Models
with Overlapping Clusters . . . . . . . 843--867
Kyoungjae Lee and
Kisung You and
Lizhen Lin Bayesian Optimal Two-Sample Tests for
High-Dimensional Gaussian Populations 869--893
Xuan Cao and
Kyoungjae Lee Consistent and Scalable Bayesian Joint
Variable and Graph Selection for Disease
Diagnosis Leveraging Functional Brain
Network . . . . . . . . . . . . . . . . 895--923
Harlan Campbell and
Paul Gustafson Defining a Credible Interval Is Not
Always Possible with ``Point-Null''
Priors: a Lesser-Known Correlate of the
Jeffreys--Lindley Paradox (with
Discussion) . . . . . . . . . . . . . . 925--984
Erica M. Porter and
Christopher T. Franck and
Marco A. R. Ferreira Objective Bayesian Model Selection for
Spatial Hierarchical Models with
Intrinsic Conditional Autoregressive
Priors . . . . . . . . . . . . . . . . . 985--1011
Mike West Perspectives on Constrained Forecasting 1013--1039
Rebecca Souza and
Lilia Costa and
Marina Paez and
João Sato and
Candida Barreto Dynamic Graphical Models with Variable
Selection for Effective Connectivity . . 1041--1065
Mingrui Liang and
Matthew D. Koslovsky and
Emily T. Hébert and
Michael S. Businelle and
Marina Vannucci Functional Concurrent Regression Mixture
Models Using Spiked Ewens--Pitman
Attraction Priors . . . . . . . . . . . 1067--1095
Giorgio Paulon and
Peter Müller and
Abhra Sarkar Bayesian Semiparametric Hidden Markov
Tensor Models for Time Varying Random
Partitions with Local Variable Selection 1097--1127
Petrus Mikkola and
Osvaldo A. Martin and
Suyog Chandramouli and
Marcelo Hartmann and
Oriol Abril Pla and
Owen Thomas and
Henri Pesonen and
Jukka Corander and
Aki Vehtari and
Samuel Kaski and
Paul-Christian Bürkner and
Arto Klami Prior Knowledge Elicitation: The Past,
Present, and Future . . . . . . . . . . 1129--1161
Anupreet Porwal and
Abel Rodríguez Laplace Power-Expected-Posterior Priors
for Logistic Regression . . . . . . . . 1163--1186
Tin D. Nguyen and
Jonathan Huggins and
Lorenzo Masoero and
Lester Mackey and
Tamara Broderick Independent Finite Approximations for
Bayesian Nonparametric Inference . . . . 1187--1224
Gwangsu Kim and
Chang D Yoo and
Yongdai Kim Bayesian Analysis of the Generalized
Additive Proportional Hazards Model:
Asymptotic Studies . . . . . . . . . . . 1225--1243
Felipe J Medina-Aguayo and
Xavier Didelot and
Richard G Everitt Speeding up Inference of Homologous
Recombination in Bacteria . . . . . . . 1245--1275
Ilsang Ohn and
Lizhen Lin and
Yongdai Kim A Bayesian Sparse Factor Model with
Adaptive Posterior Concentration . . . . 1277--1301