9780387946887 - Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions de Tanner, Martin A. (16 résultats)

Hardcover. Etat : Fair. 3rd. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

hardcover. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!

Hard Cover. Etat : Very Good. 207pp, yellow hardback, VG, small scrape along edge of the front cover, figures, equations, references, subject index, practice exercises at the end of each chapter, "introduction to a variety of computational algorithms for Bayesian and likelihood inference".

Hardcover. Etat : Très bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Etat : Very Good. 3rd. Minor shelf wear; sunning to edges of boards. Else a bright, clean copy. 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.

hardcover. Etat : Good. Book is bent.

Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9780387946887.

Hardcover. Etat : Fine. Hardcover. No noticeable cover wear. Clean unmarked text. Tight binding.

Yellow Hardback. Etat : FINE ( AS NEW). No Jacket. Graphs (illustrateur). Third Edition. 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". EX PUBLIC SECTOR LIBRARY BLACK.

hardcover. Etat : New. In shrink wrap. Looks like an interesting title!

Etat : New. In.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Hardcover. Etat : Like New. Like New. book.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 220 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 t.