Editore: China Esperanto Press, China, Beijing, 1994
ISBN 10: 7505200712 ISBN 13: 9787505200715
Lingua: Cinese
Da: 2Vbooks, Derwood, MD, U.S.A.
Trade paperback. Condizione: Fine. 75 p. Audience: General/trade. No previous owner's name. Clean, tight pages. No bent corners. No remainder mark. SC 309.
Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 105,95
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Editore: John Wiley & Sons Inc, New York, 2010
ISBN 10: 0470748265 ISBN 13: 9780470748268
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Prima edizione
Hardcover. Condizione: new. Hardcover. 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. 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. * 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 108,57
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Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 106,04
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 111,72
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EUR 101,90
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EUR 118,92
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 106,03
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 112,29
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 109,30
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 123,62
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Condizione: New.
Da: Majestic Books, Hounslow, Regno Unito
EUR 120,73
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Editore: Chapman and Hall/CRC 2023-08-02, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
EUR 109,51
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 113,45
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 185.
Editore: Taylor & Francis Ltd, London, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 24,22
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 121,05
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Aggiungi al carrelloCondizione: New. pp. xix + 357 Illus.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 129,79
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Condizione: New. pp. xix + 357.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 134,05
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Aggiungi al carrelloCondizione: 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. . . . .
Da: Revaluation Books, Exeter, Regno Unito
EUR 135,09
Quantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 168 pages. 9.19x6.13x0.47 inches. In Stock.
Editore: John Wiley & Sons Inc, New York, 2010
ISBN 10: 0470748265 ISBN 13: 9780470748268
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
Prima edizione
EUR 112,13
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. 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. * 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.