Introduction to the Numerical Solution of Markov Chains / / William J. Stewart.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Archive 1927-1999
• VerfasserIn: Stewart, William J.,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2021]; ©1995
• Year of Publication: 2021
• Language: English
• Physical Description: 1 online resource (561 p.) :; 41 line drawings 74 tables
• Other title: Frontmatter --; Contents --; Preface and Acknowledgments --; 1. Markov Chains --; 2. Direct Methods --; 3. Iterative Methods --; 4. Projection Methods --; 5. Block Hessenberg Matrices and Solution by Recursion --; 6. Decompositional Methods --; 7. P- Cyclic Markov Chains --; 8. Transient Solutions --; 9. Stochastic Automata Networks --; 10. Software --; Bibliography --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9780691223384; 9783110442496
• DOI: 10.1515/9780691223384?locatt=mode:legacy
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: William J. Stewart.

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.