Ebook Download Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions, by John Bather, J. A. Bather
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Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions, by John Bather, J. A. Bather
Ebook Download Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions, by John Bather, J. A. Bather
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Decision 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 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.
- Sales Rank: #1953430 in Books
- Brand: Brand: Wiley
- Published on: 2000-07-13
- Original language: English
- Number of items: 1
- Dimensions: 9.29" h x .67" w x 6.22" l, .91 pounds
- Binding: Hardcover
- 204 pages
- Used Book in Good Condition
Review
"This textbook...draws on his many years of experience in teaching this topic as well as on his considerable professional expertise in the area. It is ideally suited to its stated purpose as a student text." (Short Book Reviews, Vol. 20. No. 3, December 2000)
"...I was impressed with this book..." (The Statistician, Vol.51, No.2 2002)
"...excellent for the audience to whom it is addressed, and it is to be hoped that the author will write a further textbook..." (Jnl of the Operational Research Society, Vol 54(10) 2003)
From the Back Cover
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
Most helpful customer reviews
0 of 0 people found the following review helpful.
Excellent intro - but look for a used copy
By Just a Guy
Almost every other text I have seen on dynamic programming, particularly stochastic dynamic programming, and related subjects, has been way too theoretical to be of much use to me. While I'm mathematically literate, my main interest is in algorithms and applied probability (big on the "applied" part).
At least to me, this book is just about perfect. The emphasis is on dynamic programming applied to discrete optimization problems, including all the usual goodies (shortest paths, knapsack, optimal stopping, and more), although some continuous models are also included (eg. exponential, Poisson). The exposition is driven by worked examples, rather than by theorem and proof. The style is precise, but straightforward and practical - very easy to follow. How it covers so much so well in only 200 pages is remarkable.
It is however, way overpriced, so look for a good used copy.
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