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Record Nr. |
UNINA9910140556803321 |
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Autore |
Liang F (Faming), <1970-> |
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Titolo |
Advanced Markov chain Monte Carlo methods : learning from past samples / / Faming Liang, Chuanhai Liu, Raymond J. Carroll |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley, 2010 |
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ISBN |
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1-119-95680-3 |
1-282-66156-6 |
9786612661563 |
0-470-66972-1 |
0-470-66973-X |
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Descrizione fisica |
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1 online resource (379 p.) |
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Collana |
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Wiley Series in Computational Statistics |
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Altri autori (Persone) |
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LiuChuanhai <1959-> |
CarrollRaymond J |
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Disciplina |
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Soggetti |
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Monte Carlo method |
Markov processes |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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Sommario/riassunto |
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Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features:Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.A detailed discus |
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