ABSTRACT
The previous three chapters presented the scientific background necessary in order to appreciate the statistical applications that will be encountered in Chapter 4 through Chapter 9. Bayesian methods will be employed to design and analyze studies in medical diagnostics. This chapter describes Bayesian inference by introducing Bayes theorem, the foundation of the subject. This is followed with a description of the theorem: The prior information from the sample given by the likelihood function and the posterior distribution, which is the basis of all inferential techniques in Bayesian statistics. Next is a description of the main two elements of inference, namely estimation and tests of hypotheses. Also included is a demonstration of the Bayesian predictive density, another important component of inference.
