Stochastic Modelling and Applied Probability

By: Ashok P. Maitra, William Sudderth

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Hardcover | 14 March 1996

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260 Pages

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23.39 x 15.6 x 1.6

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The theory of probability began in the seventeenth century with attempts to calculate the odds of winning in certain games of chance. However, it was not until the middle of the twentieth century that mathematicians de veloped general techniques for maximizing the chances of beating a casino or winning against an intelligent opponent. These methods of finding op timal strategies for a player are at the heart of the modern theories of stochastic control and stochastic games. There are numerous applications to engineering and the social sciences, but the liveliest intuition still comes from gambling. The now classic work How to Gamble If You Must: Inequalities for Stochastic Processes by Dubins and Savage (1965) uses gambling termi nology and examples to develop an elegant, deep, and quite general theory of discrete-time stochastic control. A gambler "controls" the stochastic pro cess of his or her successive fortunes by choosing which games to play and what bets to make.

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Non-Fiction Mathematics Optimisation Game Theory Hobbies, Games & Puzzles Gambling Theories & Methods Probability & Statistics Applied Mathematics Stochastics

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Published: 17th September 2011

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Introduction	p. 1
Gambling Houses and the Conservation of Fairness	p. 5
Leavable Gambling Problems	p. 23
Nonleavable Gambling Problems	p. 59
Stationary Families of Strategies	p. 89
Approximation Theorems	p. 113
Stochastic Games	p. 171
References	p. 227
Symbol Index	p. 239
Index	p. 241
Table of Contents provided by Blackwell. All Rights Reserved.

Stochastic Modelling and Applied Probability

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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