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Stochastic Control of Hereditary Systems and Applications [electronic resource] /edited by Mou-Hsiung Chang.

by Chang, Mou-Hsiung [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Stochastic Modelling and Applied Probability: 59Publisher: New York, NY : Springer New York, 2008.Description: online resource.ISBN: 9780387758169.Subject(s): Mathematics | Differential equations, partial | Distribution (Probability theory) | Mathematical statistics | Mathematics | Probability Theory and Stochastic Processes | Partial Differential Equations | Control, Robotics, Mechatronics | Statistical Theory and MethodsDDC classification: 519.2 Online resources: Click here to access online
Contents:
and Summary -- Stochastic Hereditary Differential Equations -- Stochastic Calculus -- Optimal Classical Control -- Optimal Stopping -- Discrete Approximations -- Option Pricing -- Hereditary Portfolio Optimization.
In: Springer eBooksSummary: 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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and Summary -- Stochastic Hereditary Differential Equations -- Stochastic Calculus -- Optimal Classical Control -- Optimal Stopping -- Discrete Approximations -- Option Pricing -- Hereditary Portfolio Optimization.

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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