Bayesian Analysis
Wiley series in probability and statistics · 2012
This chapter deals with Bayesian Analysis and outlines the basics of the Bayesian approach to inference, estimation, hypothesis testing, and prediction. It briefly considers the problems of sensitivity to the prior distribution and the use of noninformative prior distributions. The chapter outlines the Bayesian decision analysis, and briefly reviews the Bayesian computational methods. It provides a brief review of some of the most important computational procedures that facilitate the implementation of Bayesian analysis, with special emphasis on simulation methods. The chapter describes two simulation-based methods, and then presents a key optimization principle in sequential problems, Bellman's dynamic programming principle, which is relevant when dealing with stochastic processes. Controlled Vocabulary Terms Bayesian statistics; hypothesis testing; stochastic processes