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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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"Extremely useful as an introduction to an interesting, developing field in statistics... I highly recommend it for the library of every well-informed statistician."
(Journal of the Am. Statistical Assoc.)
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Descrizione libro Hardcover. Condizione: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less. Codice articolo G0387940316I3N00
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Descrizione libro Hardcover. Condizione: Good. 2nd edition. Prior owner's name inked on first page upper right corner, else pages clean & bright; binding tight; minor wear to covers. 156 pages. Illustrated. Size: 6" x 9". Codice articolo S52-048915
Descrizione libro Hardcover. Condizione: Very Good. Image shows actual book for sale. Book Condition: Very Good; firm binding; contents very good; slight fading to spine. No Dust Jacket. Hard Cover Springer 1993 Mathematics. Codice articolo 112172