Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G. Monte Carlo Methods in Bayesian Computation 9780387989358

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Автор: Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G.
Название: Monte Carlo Methods in Bayesian Computation
Перевод названия: Чен Минг-Уй: Методы Монте-Карло в байесовских расчетах
ISBN: 9780387989358
Издательство: Springer
Классификация:

Вероятность и статистика

ISBN-10: 0387989358
Обложка/Формат: Hardback
Страницы: 386
Вес: 0.72 кг.
Дата издания: 2001
Серия: Springer Series in Statistics
Язык: English
Издание: 1st ed. 2000. corr.
Иллюстрации: 1, black & white illustrations
Размер: 24.23 x 16.31 x 2.46
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, 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 computions involving 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. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
Дополнительное описание: Илюстрации: 20
Круг читателей: Researchers,graduate students
Язык: eng
Издание: 1st ed. 2000. Corr. 2nd p
Оглавление: Introduction * Markov Chain Monte Carlo Sampling * Basic Monte Carlo Methods for Estimating Posterior Quantities * Estimating Marginal Posterior Densities * Estimating Ratios of Normalizing Constants * Monte Carlo Methods for Constrained Parameter Problems * Computing Bayesian Credible and HPD Intervals * Bayesian Approaches for Comparing Non-Nested Models * Bayesian Variable Section * Other Topics

Monte Carlo Methods in Bayesian Computation, Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G.