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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 |
|---|---|
| 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 |
MARC
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| 100 | 1 | |a Liang, F. |q (Faming), |d 1970- |1 https://id.oclc.org/worldcat/entity/E39PCjqWdhCH3kKHQqRTC6KVBX | |
| 245 | 1 | 0 | |a Advanced Markov chain Monte Carlo methods : |b learning from past samples / |c Faming Liang, Chuanhai Liu, Raymond J. Carroll. |
| 260 | |a Chichester, West Sussex : |b Wiley, |c 2010. | ||
| 300 | |a 1 online resource (357 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references (pages 327-352) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Advanced Markov Chain Monte Carlo Methods; Contents; Preface; Acknowledgments; Publisher's Acknowledgments; 1 Bayesian Inference and Markov Chain Monte Carlo; 2 The Gibbs Sampler; 3 The Metropolis-Hastings Algorithm; 4 Auxiliary Variable MCMC Methods; 5 Population-Based MCMC Methods; 6 Dynamic Weighting; 7 Stochastic Approximation Monte Carlo; 8 Markov Chain Monte Carlo with Adaptive Proposals; References; Index. | |
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Monte Carlo method. | |
| 650 | 0 | |a Markov processes. | |
| 650 | 2 | |a Monte Carlo Method | |
| 650 | 2 | |a Markov Chains | |
| 650 | 6 | |a Méthode de Monte-Carlo. | |
| 650 | 6 | |a Processus de Markov. | |
| 650 | 7 | |a MATHEMATICS |x Numerical Analysis. |2 bisacsh | |
| 650 | 7 | |a Markov processes |2 fast | |
| 650 | 7 | |a Monte Carlo method |2 fast | |
| 655 | 7 | |a dissertations. |2 aat | |
| 655 | 7 | |a Academic theses |2 fast | |
| 655 | 7 | |a Academic theses. |2 lcgft | |
| 655 | 7 | |a Thèses et écrits académiques. |2 rvmgf | |
| 700 | 1 | |a Liu, Chuanhai, |d 1959- |1 https://id.oclc.org/worldcat/entity/E39PCjrxw3ctYrDp3DFg7FX7jK | |
| 700 | 1 | |a Carroll, Raymond J. | |
| 758 | |i has work: |a Advanced Markov chain Monte Carlo methods (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGpbVtPyyB3b4gbXCDY773 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Liang, F. (Faming), 1970- |t Advanced Markov chain Monte Carlo methods. |d Hoboken, NJ : Wiley, 2010 |z 9780470748268 |w (DLC) 2010013148 |w (OCoLC)555629616 |
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