9780691036991-0691036993-Introduction to the Numerical Solution of Markov Chains

Introduction to the Numerical Solution of Markov Chains

ISBN-13: 9780691036991
ISBN-10: 0691036993
Edition: First Edition
Author: Stewart, William J.
Publication date: 1994
Publisher: Princeton University Press
Format: Hardcover 568 pages
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Book details

ISBN-13: 9780691036991
ISBN-10: 0691036993
Edition: First Edition
Author: Stewart, William J.
Publication date: 1994
Publisher: Princeton University Press
Format: Hardcover 568 pages

Summary

Acknowledged authors Stewart, William J. wrote Introduction to the Numerical Solution of Markov Chains comprising 568 pages back in 1994. Textbook and eTextbook are published under ISBN 0691036993 and 9780691036991. Since then Introduction to the Numerical Solution of Markov Chains textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

A cornerstone of applied probability, Markov chains can be used to help model how plants grow, chemicals react, and atoms diffuse--and applications are increasingly being found in such areas as engineering, computer science, economics, and education. To apply the techniques to real problems, however, it is necessary to understand how Markov chains can be solved numerically. In this book, the first to offer a systematic and detailed treatment of the numerical solution of Markov chains, William Stewart provides scientists on many levels with the power to put this theory to use in the actual world, where it has applications in areas as diverse as engineering, economics, and education. His efforts make for essential reading in a rapidly growing field.


Here Stewart explores all aspects of numerically computing solutions of Markov chains, especially when the state is huge. He provides extensive background to both discrete-time and continuous-time Markov chains and examines many different numerical computing methods--direct, single-and multi-vector iterative, and projection methods. More specifically, he considers recursive methods often used when the structure of the Markov chain is upper Hessenberg, iterative aggregation/disaggregation methods that are particularly appropriate when it is NCD (nearly completely decomposable), and reduced schemes for cases in which the chain is periodic. There are chapters on methods for computing transient solutions, on stochastic automata networks, and, finally, on currently available software. Throughout Stewart draws on numerous examples and comparisons among the methods he so thoroughly explains.

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