Tools for Statistical Inference Methods for the Exploration of Posterior Distributions and Likelihood Functions
Aus der Reihe
Springer Series in Statistics
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Form:Einzelkauf Download
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Sprache:Englisch
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Auflage:Second Edition 1993
Fr. 112.90
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Produktdetails
Format
Kopierschutz
Nein
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Erscheinungsdatum
06.12.2012
Verlag
Springer New YorkSeitenzahl
(Printausgabe)
Dateigröße
12552 KB
Auflage
Second Edition 1993
Sprache
Englisch
EAN
9781468401929
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.
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