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Monte Carlo Methods in Bayesian Computation (Paperback, Softcover reprint of the original 1st ed. 2000)
Loot Price: R2,992
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Monte Carlo Methods in Bayesian Computation (Paperback, Softcover reprint of the original 1st ed. 2000)
Series: Springer Series in Statistics
Expected to ship within 10 - 15 working days
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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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