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Advanced Markov chain Monte Carlo methods : learning from past samples /
This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem hav...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex :
Wiley,
2010.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples. This book includes the multicanonical algorithm, dynamic weighting, dynamically weight. |
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| Descripción Física: | 1 online resource (357 pages) |
| Bibliografía: | Includes bibliographical references (pages 327-352) and index. |
| ISBN: | 9780470669730 047066973X 9781119956808 1119956803 9780470669723 0470669721 1282661566 9781282661561 |