Bayesian Decision Problems and Markov Chains

Combines Bayesian decision theory and the theory of Markov chains by establishing a theoretical structure for Markov chains in which the transition probabilities are uncertain. Both sequential sampling and fixed sample size problems are considered. The development is largely theoretical, including questions of both existence and convergence. Problems of numerical computation are treated as they arise.