Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability & Mathematical Statistics: Applied Probability & Statistics) - Hardcover

A Markov chain is a sequence of events where the probability of each event is dependent on the event immediately preceding it, but independent of earlier events. Models and numerical equations are used to describe the patterns. This process is particularly useful in operations research and decision science for plotting the sequence of actions which will cause a system to perform optimally. This study provides a unified treatment of the theory, applications and computational methods for Markov decision processes. Important topics featured include action elimination methods, value iteration in the average reward case and sensitive discount optimality.