Bayesian Nonparametrics

ISBN-10: 0521513464
ISBN-13: 9780521513463
Edition: 2009
List price: $84.99 Buy it from $51.78
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Book details

List price: $84.99
Copyright year: 2009
Publisher: Cambridge University Press
Publication date: 4/12/2010
Binding: Hardcover
Pages: 308
Size: 7.00" wide x 10.00" long x 0.75" tall
Weight: 1.848
Language: English

Ming-Hui Chen is Professor of Statistics at the University of Connecticut; Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut; Peter Müller is Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center; Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia; and Keying Ye is Professor of Statistics at the University of Texas at San Antonio.

List of contributors
An invitation to Bayesian nonparametrics
Bayesian nonparametric methods: motivation and ideas
Introduction
Bayesian choices
Decision theory
Asymptotics
General posterior inference
Discussion
References
The Dirichlet process, related priors and posterior asymptotics
Introduction
The Dirichlet process
Priors related to the Dirichlet process
Posterior consistency
Convergence rates of posterior distributions
Adaptation and model selection
Bernshtein-von Mises theorems
Concluding remarks
References
Models beyond the Dirichlet process
Introduction
Models for survival analysis
General classes of discrete nonparametric priors
Models for density estimation
Random means
Concluding remarks
References
Further models and applications
Beta processes for survival and event history models
Quantile inference
Shape analysis
Time series with nonparametric correlation function
Concluding remarks
References
Hierarchical Bayesian nonparametric models with applications
Introduction
Hierarchical Dirichlet processes
Hidden Markov models with infinite state spaces
Hierarchical Pitman-Yor processes
The beta process and the Indian buffet process
Semiparametric models
Inference for hierarchical Bayesian nonparametric models
Discussion
References
Computational issues arising in Bayesian nonparametric hierarchical models
Introduction
Construction of finite-dimensional measures on observables
Recent advances in computation for Dirichlet process mixture models
References
Nonparametric Bayes applications to biostatistics
Introduction
Hierarchical modeling with Dirichlet process priors
Nonparametric Bayes functional data analysis
Local borrowing of information and clustering
Borrowing information across studies and centers
Flexible modeling of conditional distributions
Bioinformatics
Nonparametric hypothesis testing
Discussion
References
More nonparametric Bayesian models for biostatistics
Introduction
Random partitions
P�lya trees
More DDP models
Other data formats
An R package for nonparametric Bayesian inference
Discussion
References
Author index
Subject index

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