Description
Book SynopsisDecision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author''s fluent style will leave the reader with an avid interest in the subject.
* Tailored to the needs of students of optimization and decision theory
* Written in a lucid style with numerous examples and applications
* Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity
* Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics
* Coverage of advanced topics: Markov decision proces
Table of ContentsIntroduction;
PART I: Deterministic Models;
Multi-Stage Decision Problems;
Networks;
Further Applications;
Convexity;
PART II: Stochastic Models;
General Principles;
Optimal Stopping;
Special Problems;
PART III: Markov Decision Processes;
General Theory;
Minimising Average Costs;
Statistical Decision
