Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly-Seber model

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Abstract

Judicious choice of candidate generating distributions improves efficiency of the Metropolis-Hastings algorithm. In Bayesian applications, it is sometimes possible to identify an approximation to the target posterior distribution; this approximate posterior distribution is a good choice for candidate generation. These observations are applied to analysis of the Cormack-Jolly-Seber model and its extensions. ?? Springer Science+Business Media, LLC 2007.

Additional publication details

Publication type Conference Paper
Publication Subtype Conference Paper
Title Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly-Seber model
DOI 10.1007/s10651-007-0037-9
Volume 15
Issue 1
Year Published 2008
Language English
Larger Work Title Environmental and Ecological Statistics
First page 79
Last page 87