SELEC TOP ON CONTINUOUS-TIME CONTROL ..
Book Details
Format
Hardback or Cased Book
Book Series
Icp Advanced Texts In Mathematics
ISBN-10
1848168489
ISBN-13
9781848168480
Publisher
Imperial College Press
Imprint
Imperial College Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Mar 19th, 2012
Print length
292 Pages
Weight
552 grams
Dimensions
23.20 x 15.80 x 2.20 cms
Product Classification:
Probability & statisticsProbability and statisticsStochastics
Ksh 16,400.00
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Deals with continuous-time controlled Markov chains and Markov games. This book proposes assumptions on the control and game models that are easily verifiable (and verified) in practice. It also analyzes algorithmic and computational issues.
This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where two decision-makers (or players) try to optimize their own objective function. Both decision-making processes appear in a large number of applications in economics, operations research, engineering, and computer science, among other areas.An extensive, self-contained, up-to-date analysis of basic optimality criteria (such as discounted and average reward), and advanced optimality criteria (e.g., bias, overtaking, sensitive discount, and Blackwell optimality) is presented. A particular emphasis is made on the application of the results herein: algorithmic and computational issues are discussed, and applications to population models and epidemic processes are shown.This book is addressed to students and researchers in the fields of stochastic control and stochastic games. Moreover, it could be of interest also to undergraduate and beginning graduate students because the reader is not supposed to have a high mathematical background: a working knowledge of calculus, linear algebra, probability, and continuous-time Markov chains should suffice to understand the contents of the book.
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