Abstract: Markov chain Monte Carlo simulation techniques enable the ap plication of Bayesian methods to a variety of models where the posterior density of interest is too difficult to explore analytically. In practice, how ever, multivariate posterior densities often have characteristics which make implementation of MCMC methods more difficult. A number of techniques have been explored to help speed the convergence of a Markov chain. This paper presents a new algorithm which employs some of these techniques for cases where the target density is bounded. The algorithm is tested on sev eral known distributions to empirically examine convergence properties. It is then applied to a wildlife disease model to demonstrate real-world appli cability.
Abstract: A spatio-temporal statistical model for Chronic Wasting Disease is presented. The model has underpinnings from traditional epidemic models with differential equations and uses a Bayesian hierarchy to directly incorporate existing prevalence data. Spatial dynamics are modeled explicitly through a system of difference equations rather than through covariance. The posterior distribution gives evidence of a long term stable level of disease prevalence, and approximates the probability of the movement of the disease from one area to another. Predictions for the future of Chronic Wasting Disease in Colorado are given. The model is used to formulate efficient sampling schemes for future data collection.