Download 1 BAYESIAN STATISTICS 8 Proceedings of the Eighth Valencia

BAYESIAN
STATISTICS 8
Proceedings of the Eighth Valencia
International Meeting
June 2–6, 2006
Edited by
J. M. Bernardo
M. J. Bayarri
J. O. Berger
A. P. Dawid
D. Heckerman
A. F. M. Smith
and
M. West
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bbs8: “prelims” — 2007/4/24 — page 1 — #1
PREFACE
The Eighth Valencia International Meeting on Bayesian Statistics was held in
Benidorm (Alicante, Spain), 150 kilometres south of Valencia, from June 2nd
to June 6th 2006. The meeting was convened with the Eighth World Meeting
of the International Society for Bayesian Analysis (ISBA). Valencia/ISBA 8
continued the tradition of this premier conference series – established in 1979
with the first Valencia International Meeting – as the forum for a definitive
overview of current concerns and activities in Bayesian statistics.* In this tradition, Valencia/ISBA 8 encompassed a wide range of theoretical and applied
research, and also notably highlighted the breadth, vitality and impact of
Bayesian thinking in interdisciplinary research.
The Valencia organising committee invited 20 leading experts to present
papers, each of which was followed by discussion led by an invited discussant.
ISBA selected 32 contributed papers for plenary oral presentation, and a
further 326 papers were presented in three poster sessions. The conference
was preceded by a day of expository tutorials on Bayesian statistics.
These Proceedings contain the 20 invited papers with their discussions,
and synopses of 19 contributed papers (of which five were presented orally
and 14 as posters) that were selected by a rigorous refereeing process. The
full versions of these contributed papers appear in the Bayesian Analysis
electronic journal.
The papers cover a broad range of topics.
Foundational issues in statistics are addressed by several authors. At the
inferential interfaces, Mira and Baddeley are interested in the potential
for estimating equation methods to provide unifying opportunities in deriving statistical estimators, while Rousseau explores theoretical relationships
between null and interval testing from both Bayesian and non-Bayesian viewpoints. Objective Bayesian foundations are addressed by Wallstrom, who
revisits marginalization paradoxes and proposes resolutions in countably additive settings.
Disciplinary interface foundations are investigated in two papers. Bishop
and Lasserre discuss discriminative and generative approaches to learning,
representing interface statistics-machine learning perspectives in classification
problems. The foundations of Bayesian statistics intersect the foundations of
quantum theory, and Schäck provides an overview of recent and emergent
* The Proceedings of previous meetings have been published: the first by the University
Press, Valencia (1980); the second by North-Holland, Amsterdam (1985); and the third,
fourth, fifth, sixth and seventh by Clarendon Press, Oxford (1988, 1992, 1996, 1999, 2003).
The editors in each case were the members of the organizing committee.
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Preface
advances in interpreting and defining quantum mechanical probabilities from
a subjective Bayesian viewpoint.
Research in Bayesian non-parametrics percolates through the proceedings.
Several papers focus on extending and applying variants of Dirichlet process
models and mixtures. Gelfand, Guindani and Petrone provide a synthesis of Bayesian spatial modelling with Dirichlet processes to generate a
new class of non-parametric spatial models, while Ghahramani, Griffiths
and Sollich discuss Dirichlet process and related models for binary latent
variables applications. Theoretical developments are discussed by Kokolakis
and Kouvaris, who construct classes of continuous random measures by
mixing Dirichlet process building blocks.
Flexible models for Bayesian non-parametric regression and function fitting are the primary focus of two papers. Clyde and Wolpert develop a
class of Bayesian non-parametric regression models, and associated computational methodology, based on Lévy process priors, while Mertens develops
semi-parametric, basis-function approaches to regression with a proteomic
application.
The growth and development of objective Bayesian methods in the last
several years is reflected in a number of papers. Among these, Sun and
Berger develop and explore classes of objective priors in multilinear/normal
contexts, Bernardo and Pérez inject objective Bayesian decision theory using information-theoretic loss functions in the normal means problem, while
Cano, Kessler and Salmerón discuss objective prior specification and testing in the related one-way random effects model.
Theory and methods for model assessment and testing are the primary
focus of a number of papers. Almeida and Mouchart discuss model specification testing of a parametric null model embedded in an encompassing class
of non-parametric alternatives, while Chakrabarti and Ghosh develop theoretical studies of cross-validatory Bayes factors and related methods in model
selection for prediction. Developments in Bayesian decision theory linked to
‘large p’ multiple testing problems are addressed by Müller, Parmigiani
and Rice, motivated by genomic applications. Peruggia develops Bayesian
analysis for checking a linear hierarchical model by embedding in models with
correlated error structures, while Spitzner develops theoretical investigation
of testing under both objective and smoothness priors in the ‘large p’ normal
means problem.
In time series and forecasting, Carvalho and West introduce and develop a synthesis of matrix-variate dynamic models with graphical modelling,
illustrated in applications to Bayesian portfolio decision-making in finance.
The growth of structured probabilistic modelling for challenging problems in molecular biology, genetics and genomics is seen in several papers.
Preface
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Among these, Brooks, Manolopoulou and Emerson develop highly structured mixture models, and associated computational methods, in problems of
evolutionary molecular genetics with ecological/phylogeographic applications,
while Cowell, Lauritzen and Mortera discuss Bayesian mixture modelling
in DNA forensic analysis. Merl and Prado introduce, develop and apply
structured Bayesian models of DNA sequence data in challenging problems
of detecting natural selection, while Xing and Sohn develop hidden Markov
Dirichlet process models in studies that aim to jointly model recombination
and coalescence events in molecular population genetics.
While computational research is evident in many papers, several authors
are concerned primarily with computational questions. The evaluation of
marginal likelihood values in Bayesian analysis is addressed in two papers.
Raftery, Newton, Satagopan and Pavel develop new computational approaches to this problem based on the harmonic mean identity, while Skilling
introduces a new ‘nested sampling’ approach inspired by methods in statistical physics. Sequential computation using sequential Monte Carlo methods is broadly reviewed and developed theoretically by Del Moral, Doucet
and Jasra, while Holmes and Pintore explore sequential “relaxation” approaches for iterative development of regression and distributional models.
In the MCMC domain, Möller and Mengersen provide theoretical development of a method for estimation of posterior expectations of monotone
functions using theory of dominating processes.
Biomedical applications of Bayesian methods continue to represent a major area of success and growth of more realistic, complex statistical modelling.
Dukı́c and Dignam develop flexible multiresolution models for survival hazard functions applied to breast cancer recurrence, while Jirsa, Quinn and
Varga discuss sensitive prior modelling of response curves in radiotherapy
studies.
Bayesian research and applications in spatial statistics have expanded substantially over the last decade, and several authors address aspects of this
broad field. Practical questions of prior elicitation are developed in Denham and Mengersen in spatial/ecological contexts. Gamerman, Salazar
and Reis overview a range of recent methodological developments and introduce new spatio-temporal systems using classes of dynamic Gaussian process
models, while Ma and Carlin develop hierarchical spatial models utilizing
multivariate conditional autoregressions.
Bayesian methods in social and policy sciences are evident in several papers. Little and Zheng discuss and overview Bayesian thinking and methods
in finite population survey sampling studies, while Madrigal extends influence diagrams to incorporate experimental design intervention nodes motivated by policy decision assessment studies.
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Preface
The growth and success of complex Bayesian modelling for applications
in challenging scientific areas is reflected in several papers. Among these,
Schmidler introduces novel Bayesian models for geometric shapes, and efficient computational approaches to shape matching, with applications to problems of protein structure alignment and classification, while Short, Higdon
and Kronberg develop Bayesian process convolution and smoothing spline
models in a study of estimation of galactic magnetic fields.
Estimation of multi-regime models, including change-point problems, are
considered by several authors. Girón, Moreno and Casella revisit changepoint problems in regression from an objective Bayesian viewpoint, while
Hutter discusses MCMC and model selection questions for piecewise constant response functions.
We are most grateful to a number of organizations that provided support
for the meeting. These include the Universitat de València, the International
Society for Bayesian Analysis, Microsoft Corporation, and the National Science Foundation of the USA.
The review and selection of contributed papers for parallel publication in
this volume and Bayesian Analysis (http://ba.stat.cmu.edu) was handled
by the editorial staff of the journal. We are most grateful for the efforts of
founding editor-in-chief Rob Kass, current editor-in-chief Brad Carlin, managing editor Herbie Lee, system managing editor Pantelis Vlachos, editors Alicia
Carriquiry, Phil Dawid, David Dunson, David Heckerman, Michael Jordan,
Fabrizio Ruggeri, and Dalene Stangl, and of course numerous anonymous associate editors and referees. The papers accepted and appearing in Bayesian
Analysis are represented here by their extended synopses.
We are also most grateful to Mailo Albiach, Josefina Rodrı́guez, Lizbeth
Roman, Vera Tomazella, and Dolores Tortajada for their invaluable assistance
on matters administrative, technical and social, and in particular to Dolores
Tortajada for preparing the final LATEX version of these Proceedings.
We look forward to the Ninth Valencia International Meeting on Bayesian
Statistics, which will again be convened jointly with ISBA and is planned to
take place in the early summer of 2010.
M.J. Bayarri
J.O. Berger
J.M. Bernardo
A.P. Dawid
D. Heckerman
A.F.M. Smith
M. West
CONTENTS
I. INVITED PAPERS (with discussion)
Bishop, C. M. and Lasserre, J.: Generative or Discriminative?
Getting the Best of Both Worlds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Brooks, S. P., Manolopoulou, I. and Emerson, B. C.: Assessing the
Effect of Genetic Mutation - A Bayesian Framework for Determining
Population History from DNA Sequence Data . . . . . . . . . . . . . . . . . . . . . . . . . .
Chakrabarti, A. and Ghosh, J. K.: Some Aspects of Bayesian Model
Selection for Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Clyde, M. A. and Wolpert, R. L.: Nonparametric Function
Estimation Using Overcomplete Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . .
Del Moral, P., Doucet, A. and Jasra, A.: Sequential Monte Carlo for
Bayesian Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Gamerman, D., Salazar, E. and Reis, E. A.: Dynamic Gaussian
Process Priors, with Applications to The Analysis of Space-time Data . .
Gelfand, A. E., Guindani, M. and Petrone, S.: Bayesian Nonparametric Modelling for Spatial Data Using Dirichlet Processes . . . . . . . . . . .
Ghahramani, Z., Griffiths, T. L. and Sollich, P.: Bayesian Nonparametric Latent Feature Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Girón, F. J., Moreno, E. and Casella, G.: Objective Bayesian
Analysis of Multiple Changepoints for Linear Models. . . . . . . . . . . . . . . . . . .
Holmes, C. C. and Pintore, A.: Bayesian Relaxation: Boosting,
The Lasso, and other Lα norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Little, R. J. A. and Zheng, H.: The Bayesian Approach to the
Analysis of Finite Population Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Merl, D. and Prado, R.: Detecting selection in DNA sequences:
Bayesian Modelling and Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mira, A. and Baddeley, A.: Deriving Bayesian and frequentist
estimators from time-invariance estimating equations:
a unifying approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Müller, P., Parmigiani, G. and Rice, K.: FDR and Bayesian Multiple
Comparisons Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Raftery, A., Newton, M., Satagopan, J. and Krivitsky, P. :
Estimating the Integrated Likelihood via Posterior Simulation Using
the Harmonic Mean Identity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rousseau, J.: Approximating Interval Hypothesis: p-values and
Bayes Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Schack, R.: Bayesian Probability in Quantum Mechanics . . . . . . . . . . . . . . . .
Schmidler, S. C.: Fast Bayesian Shape Matching Using Geometric
Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Skilling, J.: Nested Sampling for Bayesian Computations . . . . . . . . . . . . . . . .
Sun, D. and Berger, J. O.: Objective Bayesian Analysis for the
Multivariate Normal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
II. CONTRIBUTED PAPERS (synopsis)
Almeida, C. and Mouchart, M.: Bayesian Encompassing Specification
Test Under Not Completely Known Partial Observability. . . . . . . . . . . . . . .
Bernardo, J. M. and Pérez, S.: Comparing Normal Means:
New Methods for an Old Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cano, J. A., Kessler, M. and Salmerón, D.: Integral Priors for the
One Way Random Effects Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Carvalho, C. M. and West, M.: Dynamic Matrix-Variate Graphical
Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cowell, R. G., Lauritzen, S.L. and Mortera, J.: A Gamma Model
for DNA Mixture Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Denham, R. J. and Mengersen, K.: Geographically Assisted Elicitation
of Expert Opinion for Regression Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dukić, V. and Dignam, J.: Hierarchical Multiresolution Hazard Model
for Breast Cancer Recurrence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hutter, M.: Bayesian Regression of Piecewise Constant Functions . . . . . . .
Jirsa, L., Quinn, A. and Varga, F.: Identification of Thyroid Gland
Activity in Radiotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kokolakis, G. and Kouvaras, G.: Partial Convexification of Random
Probability Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ma, H. and Carlin, B. P.: Bayesian Multivariate Areal Wombling . . . . . .
Madrigal, A. M.: Cluster Allocation Design Networks . . . . . . . . . . . . . . . . . . .
Mertens, B. J. A.: Logistic Regression Modelling of Proteomic Mass
Spectra in a Case-Control Study on Diagnosis for Colon Cancer . . . . . . .
Møller, J. and Mengersen, K.: Ergodic Averages Via Dominating
Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Perugia, M.: Bayesian Model Diagnostics Based on Artificial
Autoregressive Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Short, M. B., Higdon, D. M. and Kronberg, P. P.: Estimation
of Faraday Rotation Measures of the Near Galactic Sky, Using
Gaussian Process Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spitzner, D. J.: An Asymptotic Viewpoint on High-Dimensional
Bayesian Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wallstrom, T. C.: The Marginalization Paradox and Probability Limits .
Xing, E. P. and Sohn, K.-A.: A Hidden Markov Dirichlet Process
Model for Genetic Recombination in Open Ancestral Space . . . . . . . . . . . . .
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