Note on the Sampling Distribution for the Metropolis-Hastings Algorithm

The Metropolis-Hastings algorithm has been important in the recent development of Bayes methods. This algorithm generates random draws from a target distribution utilizing a sampling (or proposal) distribution. This article compares the properties of three sampling distributions-the independence cha...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2003-01, Vol.32 (4), p.775-789
Main Authors: Geweke, John, Tanizaki, Hisashi
Format: Article
Language:English
Subjects:
Distribution theory
Exact sciences and technology
Mathematics
Metropolis-Hastings algorithm
Multivariate analysis
Parametric inference
Probability and statistics
Sampling distribution
Sciences and techniques of general use
Statistics
Taylored chain
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Summary:The Metropolis-Hastings algorithm has been important in the recent development of Bayes methods. This algorithm generates random draws from a target distribution utilizing a sampling (or proposal) distribution. This article compares the properties of three sampling distributions-the independence chain, the random walk chain, and the Taylored chain suggested by Geweke and Tanizaki (Geweke, J., Tanizaki, H. ( 1999 ). On Markov Chain Monte-Carlo methods for nonlinear and non-Gaussian state-space models. Communications in Statistics, Simulation and Computation 28(4):867-894, Geweke, J., Tanizaki, H. ( 2001 ). Bayesian estimation of state-space model using the Metropolis-Hastings algorithm within Gibbs sampling. Computational Statistics and Data Analysis 37(2):151-170).
ISSN:0361-0926
1532-415X
DOI:10.1081/STA-120018828