Author: Ming-Hui Chen
Edition:
Publisher: Springer
Binding: Hardcover
ISBN: 0387989358
Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics)
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. Medical books Monte Carlo Methods in Bayesian Computation . 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 Medical books Monte Carlo Methods in Bayesian Computation. Categories: Monte Carlo method, Bayesian statistical decision theory. Contributors: Ming-Hui Chen - Author. Format: Hardcover
Download link for Monte Carlo Methods in Bayesian Computation
Categories: Monte Carlo method, Bayesian statistical decision theory. Contributors: Ming-Hui Chen - Author. Format: Hardcover
Categories: Monte Carlo method, Bayesian statistical decision theory, Monte Carlo method. Contributors: Ming-Hui Chen - Author. Format: Hardcover
Categories: Monte Carlo method, Bayesian statistical decision theory. Contributors: Ming-Hui Chen - Author. Format: Hardcover
Springer 9780387989358 Monte Carlo Methods in Bayesian Computation (2000. Corr. 2nd Edition) Description With an equal mix of theory and applications involving real data, this book presents the theoretical background of the Markov chain Monte Carlo (MCMC) methods and examines advanced Bayesian computational methods. 20 illus. *Author: Chen, Ming-Hui/ Chen, MH/ Ibrahim, JG *Series Title: Springer Series in Statistics *Binding Type: Hardcover *Number of Pages: 387 *Publication Date: 2000/01
Medical Book Monte Carlo Methods in Bayesian Computation
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.
