ABSTRACT
This chapter describes Bayesian inferential techniques to gain a deeper understanding of the mechanism of various biological phenomena. It illustrates several examples the Bayesian approach to making inferences: an example of inbreeding in genetics, the general birth and death process, the logistic growth process and a simple model for an epidemic. It also includes the chain binomial model and Greenwood and the Reed–Frost versions of the epidemic models, several genetic models, including the Wright model and the Ehrenfest model for diffusion through a membrane. Bayesian inferences will include determining the posterior distribution of the relevant parameters, testing hypotheses about those parameters, and determining the Bayesian predictive distribution of future observations. The chapter deals with that using Markov chains to model the evolution of an epidemic then explores the use of Bayesian techniques to provide inferences for the unknown transition probabilities of the process. The Ehrenfest model is an interesting example of the transfer of a model from physics to biology.
