Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions / Edition 1by John Bather
Pub. Date: 08/03/2000
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 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 processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters
- Contains exercises at the end of each chapter, with hints in an appendix
- Publication date:
- Wiley Interscience Series in Systems and Optimization Series , #10
- Product dimensions:
- 6.22(w) x 9.29(h) x 0.67(d)
Table of ContentsDETERMINISTIC MODELS.
Multi-Stage Decision Problems.
MARKOV DECISION PROCESSES.
Minimizing Average Costs.
Notes on the Exercises.
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