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130626 ||| eng |
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|a 9780387758169
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|a Chang, Mou-Hsiung
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|a Stochastic Control of Hereditary Systems and Applications
|h Elektronische Ressource
|c by Mou-Hsiung Chang
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| 250 |
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|a 1st ed. 2008
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| 260 |
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|a New York, NY
|b Springer New York
|c 2008, 2008
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| 300 |
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|a XVIII, 406 p
|b online resource
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| 505 |
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|a and Summary -- Stochastic Hereditary Differential Equations -- Stochastic Calculus -- Optimal Classical Control -- Optimal Stopping -- Discrete Approximations -- Option Pricing -- Hereditary Portfolio Optimization
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Statistics
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| 653 |
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|a Probability Theory
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| 653 |
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|a Control engineering
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| 653 |
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|a Robotics
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| 653 |
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|a Differential Equations
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| 653 |
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|a Automation
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| 653 |
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|a Differential equations
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| 653 |
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|a Probabilities
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Stochastic Modelling and Applied Probability
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| 028 |
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|a 10.1007/978-0-387-75816-9
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| 856 |
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|u https://doi.org/10.1007/978-0-387-75816-9?nosfx=y
|x Verlag
|3 Volltext
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|a 519.2
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|a This research monograph develops the Hamilton-Jacobi-Bellman (HJB) theory through dynamic programming principle for a class of optimal control problems for stochastic hereditary differential systems. It is driven by a standard Brownian motion and with a bounded memory or an infinite but fading memory. The optimal control problems treated in this book include optimal classical control and optimal stopping with a bounded memory and over finite time horizon. This book can be used as an introduction for researchers and graduate students who have a special interest in learning and entering the research areas in stochastic control theory with memories. Each chapter contains a summary. Mou-Hsiung Chang is a program manager at the Division of Mathematical Sciences for the U.S. Army Research Office
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