Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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In den WarenkorbZustand: New.
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
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In den WarenkorbZustand: New. pp. xix + 357 Illus.
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In den WarenkorbZustand: NEW.
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In den WarenkorbHardcover. Zustand: Brand New. 168 pages. 9.19x6.13x0.47 inches. In Stock.
Zustand: New. 2023. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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In den WarenkorbZustand: New. * Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms. Series: Wiley Series in Computational Statistics. Num Pages: 378 pages, Illustrations. BIC Classification: PBKS. Category: (P) Professional & Vocational. Dimension: 159 x 233 x 26. Weight in Grams: 724. . 2010. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Zustand: New. Dr. Faming Liang is Distinguished Professor of Statistics, Purdue University. Prior joining Purdue University in 2017, he held regular faculty positions in the Department of Biostatistics, University of Florida and Department of Statisti.
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In den WarenkorbHardcover. Zustand: Brand New. 168 pages. 9.19x6.13x0.47 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 183,58
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 378 pages. 9.00x6.00x0.75 inches. In Stock.
Buch. Zustand: Neu. Neuware - 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 discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.\* Up-to-date accounts of recent developments of the Gibbs sampler.\* Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.\* Accompanied by a supporting website featuring datasets used in the book, along with codes used for some simulation examples.This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.
Anbieter: preigu, Osnabrück, Deutschland
Buch. Zustand: Neu. Sparse Graphical Modeling for High Dimensional Data | A Paradigm of Conditional Independence Tests | Faming Liang (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9780367183738 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.