Further Bayesian and Monte Carlo Recapture Methods

Abstract

Although Bayesian methods are sprinkled throughout some of the previous chapters, we now give the topic our full attention and add some extensions. Their particular advantage arises from being able to apply Markov chain Monte Carlo techniques along with so-called reversible jump methods to sample from posterior distributions. We then consider modeling associations between parameters beginning with survival and birth (emergence), and then extending to deal with density dependence, covariates, and migration. Random effects can be better dealt with using a Bayesian model and Markov chain Monte Carlo simulations for the CJS (Cormack–Jolly–Seber) model. We finally describe a general method that is gaining popularity called data augmentation that is mentioned again in later chapters.