Note on the sampling distribution for the Metropolis-Hastings algorithm

Geweke, J; Tanizaki, H

Note on the sampling distribution for the Metropolis-Hastings algorithm

Geweke, J Tanizaki, H

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Publication Type:: Journal Article
Citation:: Communications in Statistics - Theory and Methods, 2003, 32 (4), pp. 775 - 789
Issue Date:: 2003-01-01

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Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.contributor.author	Tanizaki, H	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Communications in Statistics - Theory and Methods, 2003, 32 (4), pp. 775 - 789	en_US
dc.identifier.issn	0361-0926	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8304
dc.description.abstract	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).	en_US
dc.relation.ispartof	Communications in Statistics - Theory and Methods	en_US
dc.relation.isbasedon	10.1081/STA-120018828	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Note on the sampling distribution for the Metropolis-Hastings algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	32	en_US
utslib.for	010405 Statistical Theory	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

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).

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http://hdl.handle.net/10453/8304