Tools for Statistical Inference

Methods for the Exploration of Posterior Distributions and Likelihood Functions

  • Martin A. Tanner

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Martin A. Tanner
    Pages 1-13
  3. Martin A. Tanner
    Pages 64-89
  4. Martin A. Tanner
    Pages 90-136
  5. Back Matter
    Pages 193-208

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.


Bayesian inference Gibbs sampler Likelihood Resampling algorithms expectation–maximization algorithm observed data

Authors and affiliations

  • Martin A. Tanner
    • 1
  1. 1.Department of StatisticsNorthwestern UniversityEvanstonUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8471-0
  • Online ISBN 978-1-4612-4024-2
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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