, Volume 15, Issue 1, pp 79-87
Date: 15 Sep 2007

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.