Using capture-recapture data and hybrid Monte Carlo sampling to estimate an animal population affected by an environmental catastrophe

Guoqi Qian, Ning Li, Richard Huggins

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

We propose a dynamic model for the evolution of an open animal population that is subject to an environmental catastrophe. The model incorporates a capture-recapture experiment often conducted for studying wildlife population, and enables inferences on the population size and possible effect of the catastrophe. A Bayesian approach is used to model unobserved quantities in the problem as latent variables and Markov chain Monte Carlo (MCMC) is used for posterior computation. Because the particular interrelationship between observed and latent variables negates the feasibility of standard MCMC methods, we propose a hybrid Monte Carlo approach that integrates a Gibbs sampler with the strategies of sequential importance sampling (SIS) and acceptance-rejection (AR) sampling for model estimation. We develop results on how to construct effective proposal densities for the SIS scheme. The approach is illustrated through a simulation study, and is applied to data from a mountain pygmy possum (Burramys Parvus) population that was affected by a bushfire.

Original languageEnglish
Pages (from-to)655-666
Number of pages12
JournalComputational Statistics and Data Analysis
Volume55
Issue number1
DOIs
Publication statusPublished - 1 Jan 2011
Externally publishedYes

Keywords

  • Acceptance-rejection sampling
  • Capture recapture
  • Gibbs sampler
  • Population size
  • Sequential importance sampling

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