Introduction to the Numerical Solution of Markov Chains:
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...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Princeton, NJ
Princeton University Press
[2021]
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| Online-Zugang: | FAW01 FAB01 FCO01 FHA01 FKE01 FLA01 UPA01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Nov 2021) |
| Beschreibung: | 1 Online-Ressource (561 pages) 41 line drawings 74 tables |
| ISBN: | 9780691223384 |
| DOI: | 10.1515/9780691223384 |
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| spelling | Stewart, William J. Verfasser aut Introduction to the Numerical Solution of Markov Chains William J. Stewart Princeton, NJ Princeton University Press [2021] © 1995 1 Online-Ressource (561 pages) 41 line drawings 74 tables txt rdacontent c rdamedia cr rdacarrier Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Nov 2021) 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 In English MATHEMATICS / Probability & Statistics / Stochastic Processes bisacsh Markov processes Numerical solutions https://doi.org/10.1515/9780691223384 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Stewart, William J. Introduction to the Numerical Solution of Markov Chains MATHEMATICS / Probability & Statistics / Stochastic Processes bisacsh Markov processes Numerical solutions |
| title | Introduction to the Numerical Solution of Markov Chains |
| title_auth | Introduction to the Numerical Solution of Markov Chains |
| title_exact_search | Introduction to the Numerical Solution of Markov Chains |
| title_exact_search_txtP | Introduction to the Numerical Solution of Markov Chains |
| title_full | Introduction to the Numerical Solution of Markov Chains William J. Stewart |
| title_fullStr | Introduction to the Numerical Solution of Markov Chains William J. Stewart |
| title_full_unstemmed | Introduction to the Numerical Solution of Markov Chains William J. Stewart |
| title_short | Introduction to the Numerical Solution of Markov Chains |
| title_sort | introduction to the numerical solution of markov chains |
| topic | MATHEMATICS / Probability & Statistics / Stochastic Processes bisacsh Markov processes Numerical solutions |
| topic_facet | MATHEMATICS / Probability & Statistics / Stochastic Processes Markov processes Numerical solutions |
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