Markov Random Field Modeling in Image Analysis, Stan Z. Li

Автор: Martin L. Puterman
Название: Markov Decision Processes: Discrete Stochastic Dynamic Programming
ISBN: 0471727822 ISBN-13(EAN): 9780471727828
Издательство: Wiley
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Цена: 20584.00 р.
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Описание: This book is an up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. The concentration of the book is on infinite-horizon discrete-time models, and it also discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models.

Автор: Fleming Wendell H., Soner H.M.
Название: Controlled Markov Processes and Viscosity Solutions
ISBN: 0387260455 ISBN-13(EAN): 9780387260457
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. New chapters in this second edition introduce the role of stochastic optimal control in portfolio optimization and in pricing derivatives in incomplete markets and two-controller, zero-sum differential games.

Автор: Stroock Daniel W.
Название: An Introduction to Markov Processes
ISBN: 3540234519 ISBN-13(EAN): 9783540234517
Издательство: Springer
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Цена: 8384.00 р.
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Описание: This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. A whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium.

Автор: Ching Wai-Ki, Ng Michael K.
Название: Markov Chains: Models, Algorithms and Applications
ISBN: 0387293353 ISBN-13(EAN): 9780387293356
Издательство: Springer
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Цена: 13270.00 р.
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Описание: Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This title outlines developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, and many other practical systems.