Bayesian population size estimation using Dirichlet process mixtures

We introduce a new Bayesian nonparametric method for estimating the size of a closed population from multiple-recapture data. Our method, based on Dirichlet process mixtures, can accommodate complex patterns of heterogeneity of capture, and can transparently modulate its complexity without a separate model selection step. Additionally, it can handle the massively sparse contingency tables generated by large number of recaptures with moderate sample sizes. We develop an efficient and scalable MCMC algorithm for estimation. We apply our method to simulated data, and to two examples from the literature of estimation of casualties in armed conflicts.