Tools for Statistical Inferface : Methods for the Exploration of Posterior Distributions and Likelihood Functions

Tanner, Martin A.

ISBN 10: 0387946888 / ISBN 13: 9780387946887

Published by Springer, London, 1993

About this Item

This book is part of a large purchase from a Public Sector Library and except where mentioned are for the most part LIKE NEW! MOSTLY the ONLY flaws are the blacked out (they insisted) Library stamps which show many of them to be UNUSED! FEEL FREE TO E-MAIL FOR PHOTOGRAPHS AND FURTHER DETAILS. FROM A DEALER WHO TELLS YOU WHO THEY ARE AND WHAT THEIR TELEPHONE AND ADDRESS CONTACT DETAILS ARE! Size: 8vo - over 7¾" - 9¾".

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Bibliographic Details

• Title: Tools for Statistical Inferface : Methods for the Exploration of Posterior Distributions and Likelihood Functions
• Publisher: Springer, London
• Publication Date: 1993
• Language: English
• ISBN 10: 0387946888
• ISBN 13: 9780387946887
• Binding: Yellow Hardback
• Illustrator: Graphs
• Edition: Third Edition
• Condition: FINE ( AS NEW)
• Dust Jacket Condition: No Jacket
• Signed: EX PUBLIC SECTOR LIBRARY BLACK
• Seller catalogs:
  • Environmental Subjects

About this title

Synopsis

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.