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Monte Carlo Methods in Bayesian Computation /

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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...

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Bibliographic Details
Main Author: Chen, Minghui
Corporate Author: SpringerLink (Online service)
Other Authors: Shao, Qi-Man, Ibrahim, Joseph G.
Format: eBook
Language:English
Published: New York, NY : Springer New York, 2000.
Series:Springer series in statistics.
Subjects:
Statistics.
Mathematical statistics.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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.