9780470046098 - understanding computational bayesian statistics: 644 di bolstad, william m. (21 risultati)
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Condizione: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. pp. xiv + 315 Illus.
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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Hardback. Condizione: New. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach…. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distributionThe distributions from the one-dimensional exponential familyMarkov chains and their long-run behaviorThe Metropolis-Hastings algorithmGibbs sampling algorithm and methods for speeding up convergenceMarkov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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Condizione: New. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. Series:…Wiley Series in Computational Statistics. Num Pages: 336 pages, Illustrations. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 241 x 153 x 25. Weight in Grams: 604. . 2009. 1st Edition. Hardcover. . . . .
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. pp. xiv + 315 Index.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. William M. Bolstad, PhD, is Senior Lecturer in the Department of Statistics at The University of Waikato (New Zealand). Dr. Bolstad s research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time se.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 204,41
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Hardcover. Condizione: Brand New. 1st edition. 336 pages. 9.50x6.00x0.75 inches. In Stock.
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Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Condizione: New. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. Series:…Wiley Series in Computational Statistics. Num Pages: 336 pages, Illustrations. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 241 x 153 x 25. Weight in Grams: 604. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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Hardback. Condizione: New. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach…. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distributionThe distributions from the one-dimensional exponential familyMarkov chains and their long-run behaviorThe Metropolis-Hastings algorithmGibbs sampling algorithm and methods for speeding up convergenceMarkov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 610.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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Hardcover. Condizione: new. Hardcover. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-e…dge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distributionThe distributions from the one-dimensional exponential familyMarkov chains and their long-run behaviorThe Metropolis-Hastings algorithmGibbs sampling algorithm and methods for speeding up convergenceMarkov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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- Print on Demand
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 190,55
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Hardcover. Condizione: Brand New. 1st edition. 336 pages. 9.50x6.00x0.75 inches. In Stock. This item is printed on demand.
