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"This book provides a unified presentation of a variety of computational algorithms which are used in likelihood and Bayesian inference. In this second edition, Martin Tanner has taken the opportunity to expand the treatment of many of the techniques discussed, to devote more space to comparing the methods covered, and to describe the applications in more detail. Topics covered include: maximum likelihood, Monte Carlo methods, the EM algorithm, data augmentation techniques, imputation methods, the Gibbs sampler, the Metropolis algorithm, and the griddy Gibbs sampler. The reader is assumed to have a reasonable basic background in statistics as might be gained in the first year of a graduate course, but otherwise the book is self-contained. As a result, the book will provide an invaluable survey of the fast-moving area of statistics for research statisticians and for other researchers and graduate students whose research touches on these techniques."
"This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. The third edition expands the discussion of many of the techniques discussed, includes additional examples, and adds exercise sets at the end of each chapter."
"From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#"

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"Electronic books"

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"Tools for statistical inference : observed data and data augmentation methods"
"Tools for statistical inference : observed data and data augmentation methods"@en
"Tools for statistical inference : methods for the exploration of posterior distributions and Likelhood functions"@en
"Tools for statistical inference methods for the exploration of posterior distributions and likelihood functions"
"Tools for statistical inference methods for the exploration of posterior distributions and likelihood functions"@en
"Tools for statistical inference"
"Tools for statistical inference : methods for the Exploration of Posterior Distributions and Likelihood Functions"@en
"Tools for statistical inference : observed data augmentation methods"
"Tools for statistical inference : methods for the explorations of posterior distribution and likelihood functions"
"Tools for statistical inference : observed data and data argumentation methods"
"Tools for statistical inference : Observed data and data augmentation methods"
"Tools for statistical inference : methods for the exploration of posterior distributions and Likelihood functions"
"Tools for Statistical Inference Observed Data and Data Augmentation Methods"
"Tools for statistical interference : methods for the exploration of posterior distributions and likelihood functions"
"Tools for statistical inference observed data and data augmentation methods"@en
"Tools for Statistical Inference Methods for the Exploration of Posterior Distributions and Likelihood Functions"
"Tools for Statistical Inference Methods for the Exploration of Posterior Distributions and Likelihood Functions"@en
"Tools for statistical inference : methods for the exploration of posterior distributions and likelihood functions"
"Tools for statistical inference : methods for the exploration of posterior distributions and likelihood functions"@en
"Tools for Statistical Inference : Methods for the Exploration of Posterior Distributions and Likelihood Functions"

workExample

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