Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions

Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multi–stage 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 theoryWritten in a lucid style with numerous examples and applicationsCoverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexityCoverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topicsCoverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parametersContains exercises at the end of each chapter, with hints in an appendixAimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.

Spis treści:
Introduction;
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

Okładka tylna:
Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multi–stage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems ar e 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 theoryWritten in a lucid style with numerous examples and applicationsCoverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexityCoverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topicsCoverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parametersContains exercises at the end of each chapter, with hints in an appendixAimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.