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Transdimensional approximate Bayesian computation for inference on invasive species models with latent variables of unknown dimension

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  • Chkrebtii, Oksana A.
  • Cameron, Erin K.
  • Campbell, David A.
  • Bayne, Erin M.

Abstract

Accurate information on patterns of introduction and spread of non-native species is essential for making predictions and management decisions. In many cases, estimating unknown rates of introduction and spread from observed data requires evaluating intractable variable-dimensional integrals. In general, inference on the large class of models containing latent variables of large or variable dimension precludes the use of exact sampling techniques. Approximate Bayesian computation (ABC) methods provide an alternative to exact sampling but rely on inefficient conditional simulation of the latent variables. To accomplish this task efficiently, a new transdimensional Monte Carlo sampler is developed for approximate Bayesian model inference and used to estimate rates of introduction and spread for the non-native earthworm species Dendrobaena octaedra (Savigny) along roads in the boreal forest of northern Alberta. Using low and high estimates of introduction and spread rates, the extent of earthworm invasions in northeastern Alberta is simulated to project the proportion of suitable habitat invaded in the year following data collection.

Suggested Citation

  • Chkrebtii, Oksana A. & Cameron, Erin K. & Campbell, David A. & Bayne, Erin M., 2015. "Transdimensional approximate Bayesian computation for inference on invasive species models with latent variables of unknown dimension," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 97-110.
  • Handle: RePEc:eee:csdana:v:86:y:2015:i:c:p:97-110
    DOI: 10.1016/j.csda.2015.01.002
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    References listed on IDEAS

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    1. Congdon, Peter, 2006. "Bayesian model choice based on Monte Carlo estimates of posterior model probabilities," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 346-357, January.
    2. Paul Fearnhead & Dennis Prangle, 2012. "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 419-474, June.
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