Capture-recapture estimation using finite mixtures of arbitrary dimension

Richard Arnold*, Yu Hayakawa, Paul Yip

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Reversible jump Markov chain Monte Carlo (RJMCMC) methods are used to fit Bayesian capture-recapture models incorporating heterogeneity in individuals and samples. Heterogeneity in capture probabilities comes from finite mixtures and/or fixed sample effects allowing for interactions. Estimation by RJMCMC allows automatic model selection and/or model averaging. Priors on the parameters stabilize the estimates and produce realistic credible intervals for population size for overparameterized models, in contrast to likelihood-based methods. To demonstrate the approach we analyze the standard Snowshoe hare and Cottontail rabbit data sets from ecology, a reliability testing data set.

Original languageEnglish
Pages (from-to)644-655
Number of pages12
Issue number2
Publication statusPublished - 2010 Jun 1


  • Bayesian model averaging
  • Capture-recapture
  • Closed populations
  • Heterogeneity
  • Mixture distribution
  • Reversible jump MCMC

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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