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About this book
This book provides a unified introduction to a variety of computational algorithms for Bayesian and likelihood inference. In this third edition, I have attempted to expand the treatment of many of the techniques discussed. I have added some new examples, as well as included recent results. Exercises have been added at the end of each chapter. Prerequisites for this book include an understanding of mathematical statistics at the level of Bickel and Doksum (1977), some understanding of the Bayesian approach as in Box and Tiao (1973), some exposure to statistical models as found in McCullagh and NeIder (1989), and for Section 6. 6 some experience with condi tional inference at the level of Cox and Snell (1989). I have chosen not to present proofs of convergence or rates of convergence for the Metropolis algorithm or the Gibbs sampler since these may require substantial background in Markov chain theory that is beyond the scope of this book. However, references to these proofs are given. There has been an explosion of papers in the area of Markov chain Monte Carlo in the past ten years. I have attempted to identify key references-though due to the volatility of the field some work may have been missed.
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Table of contents (6 chapters)
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Bibliographic Information
Book Title: Tools for Statistical Inference
Book Subtitle: Methods for the Exploration of Posterior Distributions and Likelihood Functions
Authors: Martin A. Tanner
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-1-4612-4024-2
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag New York, Inc. 1996
Hardcover ISBN: 978-0-387-94688-7Published: 26 June 1996
Softcover ISBN: 978-1-4612-8471-0Published: 27 September 2011
eBook ISBN: 978-1-4612-4024-2Published: 06 December 2012
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 3
Number of Pages: VIII, 208
Topics: Applications of Mathematics