Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics)

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Ming-Hui Chen 作者
Springer
译者
2001-10-05 出版日期
400 页数
USD 95.00 价格
Hardcover
Springer Series in Statistics 丛书系列
9780387989358 图书编码

Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 图书标签: 机器学习  蒙特卡罗  贝叶斯  抽样方法  MachineLearning   


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Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) 在线电子书 图书描述

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.

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