Bayesian statistics is one of the active research areas in statistics. This book provides the theoretical background behind the important development, Markov chain Monte Carlos methods. Series: Springer Series in Statistics. Num Pages: 400 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 23. Weight in Grams: 720. . 2000. 1st ed. 2000. Corr. 2nd printing 2001. Hardback. . . . .

N� de ref. del art�culo V9780387989358

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Detalles bibliogr�ficos

T�tulo: Monte Carlo Methods in Bayesian Computation
Editor: Springer-Verlag New York Inc.
Fecha de publicaci�n: 2000
Idioma: Ingl�s
ISBN 10: 0387989358
ISBN 13: 9780387989358
Encuadernaci�n: Encuadernaci�n de tapa dura
Edici�n: 1� Edici�n
Estado: New

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Sinopsis

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.

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"This book combines the theory topics with good computer and application examples from the field of food science, agriculture, cancer and others. The volume will provide an excellent research resource for statisticians with an interest in computer intensive methods for modelling with different sorts of prior information."
A.V. Tsukanov in "Short Book Reviews", Vol. 20/3, December 2000

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