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Da: Majestic Books, Hounslow, Regno Unito
EUR 173,63
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Lingua: Inglese
Editore: Chapman and Hall/CRC 2011-05-10, 2011
ISBN 10: 1420079417 ISBN 13: 9781420079418
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Aggiungi al carrelloHardcover. Condizione: Brand New. 1st edition. 619 pages. 10.16x7.24x1.34 inches. In Stock.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2026
ISBN 10: 1032591579 ISBN 13: 9781032591575
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. This thoroughly revised and expanded second edition of the Handbook of Markov Chain Monte Carlo reflects the dramatic evolution of MCMC methods since the publication of the first edition. With the addition of two new editors, Radu V. Craiu and Dootika Vats, this comprehensive reference now offers deeper insights into the theoretical foundations and cutting-edge developments that are reshaping the field.Key Features:Completely restructured content with 13 updated chapters from the first edition and ten entirely new chapters reflecting the latest methodological advancesIn-depth coverage of recent breakthroughs in multi-modal sampling, intractable likelihood problems, and involutive MCMC theoryComprehensive exploration of unbiased MCMC methods, control variates, and rigorous convergence boundsPractical guidance on implementing MCMC algorithms on modern hardware and software platformsCutting-edge material on the integration of MCMC with deep learning and other machine learning approachesAuthoritative treatment of theoretical foundations alongside practical implementation strategiesSupplemented by a GitHub repository including sample chapters, code, and dataThis essential reference serves statisticians, computer scientists, physicists, data scientists, and researchers across disciplines who employ computational methods for Bayesian inference and stochastic simulation. Graduate students will find it an invaluable learning resource, while experienced practitioners will appreciate its balance of theoretical depth and practical implementation advice. Whether used as a comprehensive guide to current MCMC methodology or as a reference for specific advanced techniques, this handbook provides the definitive resource for anyone working at the intersection of Bayesian computation and modern statistical modeling.