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Monte Carlo Methods in Bayesian Computation
Chen, Ming-Hui; Shao, Qi-Man; Ibrahim, Joseph G.
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Description for Monte Carlo Methods in Bayesian Computation
Hardback. 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.
Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations ... Read more
Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations ... Read more
Product Details
Format
Hardback
Publication date
2000
Publisher
Springer-Verlag New York Inc. United States
Number of pages
400
Condition
New
Series
Springer Series in Statistics
Number of Pages
387
Place of Publication
New York, NY, United States
ISBN
9780387989358
SKU
V9780387989358
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Monte Carlo Methods in Bayesian Computation
"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