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

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Series: Stochastic Modelling and Applied Probability ; 59Publisher: New York, NY : Springer New York, 2008Description: XVIII, 406 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9780387758169

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

519.2 23

LOC classification:

QA273.A1-274.9
QA274-274.9

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.

Item type:

eBooks

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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.