In this paper we consider the problem of Bayesian model determination in the context of the analysis of multiple-site capture-recapture. Extending the work of Dupuis (1995), we motivate a range of biologically plausible models and show how the original Gibbs sampling algorithm of Dupuis can be extended to obtain posterior model probabilities through the introduction of reversible jump Markov chain Monte Carlo updates.
This model selection procedure improves upon previous analyses in two distinct ways. First, if parameter estimates are of primary interest, then Bayesian model averaging provides a robust estimation technique which properly incorporates model uncertainty in the resulting intervals. Second, by discriminating between competing models, we are able to discern fine structure within the data e.g., whether or not survival depends upon age, year or location. Such questions are often of primary biological importance and can only be addressed through model comparison techniques.
We examine the lizard data discussed in Dupuis (1995) and show that most of the posterior mass is placed upon models not previously considered for this data. We discuss model discrimination and model averaging and focus upon the increased scientific understanding of the data obtained via the Bayesian model comparison procedure.
Appeared as King, R. and Brooks, S.P. (2002) "Bayesian Model Discrimination for Multiple Strata Capture-Recapture Data". Biometrika 89 pp 785-806