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-ix
  2. Martin A. Tanner
    Pages 1-8
  3. Martin A. Tanner
    Pages 38-57
  4. Martin A. Tanner
    Pages 58-101
  5. Back Matter
    Pages 147-157

About this book


This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. In this second edition, I have attempted to expand the treatment of many of the techniques dis­ cussed, as well as include important topics such as the Metropolis algorithm and methods for assessing the convergence of a Markov chain algorithm. 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), experience with condi­ tional inference at the level of Cox and Snell (1989) and exposure to statistical models as found in McCullagh and Neider (1989). I have chosen not to present the proofs of convergence or rates of convergence since these proofs may require substantial background in Markov chain theory which is beyond the scope ofthis 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 last five years. I have attempted to identify key references - though due to the volatility of the field some work may have been missed.


Area Bayesian inference Gibbs sampler Likelihood Monte Carlo method Statistica algorithms computation expectation–maximization algorithm observed data presentation statistical inference statistics techniques tool

Authors and affiliations

  • Martin A. Tanner
    • 1
  1. 1.RochesterUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1993
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4684-0194-3
  • Online ISBN 978-1-4684-0192-9
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site