Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models

Journal of Ornithology
By:  and 



Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model's assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models (M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects.
Publication type Article
Publication Subtype Journal Article
Title Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models
Series title Journal of Ornithology
DOI 10.1007/s10336-010-0619-4
Volume 152
Issue Supplement 2
Year Published 2012
Language English
Publisher Springer
Publisher location Amsterdam, Netherlands
Contributing office(s) Patuxent Wildlife Research Center
Description 17 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Ornithology
First page 521
Last page 537
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