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Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Contributor(s): Bather, John (Author)
ISBN: 0471976490     ISBN-13: 9780471976493
Publisher: Wiley
OUR PRICE:   $128.20  
Product Type: Paperback - Other Formats
Published: July 2000
Qty:
Annotation: 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 authors 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
Aimed 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.
Additional Information
BISAC Categories:
- Mathematics | Linear & Nonlinear Programming
- Mathematics | Probability & Statistics - General
- Mathematics | Discrete Mathematics
Dewey: 519.703
LCCN: 00029620
Series: Wiley Interscience Systems and Optimization
Physical Information: 0.45" H x 6.22" W x 9.2" (0.68 lbs) 208 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
Die Anwendung des Induktionsprinzips auf die L sung von Optimierungsproblemen ist gegenw rtig gefragt und wird viel diskutiert. Der Autor dieses Bandes beginnt bei einem historischen Abri Ÿ und beschreibt anschlie Ÿend deterministische Modelle, in denen die Wahl zwischen zwei M glichkeiten nicht vom Zufall beeinflu Ÿt wird. Im zweiten Teil wird der Unsicherheitsfaktor einbezogen; der dritte Teil befa Ÿt sich mit speziellen, fortgeschrittenen Anwendungen, beispielsweise Markov-Prozessen. (04/00)