Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination (1995)

by Peter J. Green

Venue:	Biometrika
Citations:	577 - 18 self

Datum	Value	Source
TITLE	Reversible jump Markov chain Monte Carlo computation and Bayesian model	SVM HeaderParse 0.2
AUTHOR NAME	Peter J. Green	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Mathematics; University of Bristol; Summary	SVM HeaderParse 0.2
AUTHOR ADDR	; Bristol BS8 1TW, UK	SVM HeaderParse 0.2
ABSTRACT	Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This article proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and to a Bayesian comparison of binomial experiments.	SVM HeaderParse 0.2
CITATIONS	22 found	ParsCit 1.0