Environmental and Ecological Statistics

, Volume 15, Issue 1, pp 79–87 | Cite as

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

Article

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.

Keywords

Cormack–Jolly–Seber model Mark-recapture analysis Markov chain Monte Carlo Metropolis-Hastings algorithm 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.USGS Patuxent Wildlife Research CenterLaurelUSA
  2. 2.Department of Mathematics and StatisticsUniversity of OtagoDunedinNew Zealand

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