ISBN: 9781441928283 - a first course in bayesian statistical methods (28 risultati)

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Paperback. Condizione: Very Good. A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.…

Paperback. Condizione: Very Good. A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.…

paperback. Condizione: Very Good.

paperback. Condizione: New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Softcover reprint of hardcover 1st ed. 2009.

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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Paperback. Condizione: New. * A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. * Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. * The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods. Softcover reprint of hardcover 1st ed. 2009.…

Condizione: New.

Couverture souple. Condizione: Comme neuf. ** France : Par défaut Livraison STANDARD en Relais Colis / PRIORITAIRE : Livraison à domicile ** Springer verlag, New York, 2010. Language: English. Softcover, 8vo, 270 pp. This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method. Cover and pages clean, a copy in mint condition. // Springer verlag, New York, 2010. Texte en Anglais. In-8 broché, 270 pp. Une introduction à la théorie et à l'application des méthodes statistiques bayésiennes. Accessible à ceux qui ont une connaissance de base des probabilités, tout en permettant aux lecteurs avancés de comprendre les principes qui sous-tendent la théorie et la méthode bayésiennes. Couv. et pages propres, exemplaire en excellent état. - ISBN 9781441928283 - Weight / Poids livre : 468 g, ép. 16 mm. Livre.…

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Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Condizione: New.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice.Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.Peter Hoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for multivariate data, including covariance and copula estimation, cluster analysis, mixture modeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics.…

Taschenbuch. Condizione: Neu. A First Course in Bayesian Statistical Methods | Peter D. Hoff | Taschenbuch | Springer Texts in Statistics | ix | Englisch | 2010 | Springer | EAN 9781441928283 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Condizione: New. * A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. * Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. * The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods. Softcover reprint of hardcover 1st ed. 2009.…

Condizione: new. Questo è un articolo print on demand.

PF. Condizione: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice.Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.Peter Hoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for multivariate data, including covariance and copula estimation, cluster analysis, mixture modeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics. 284 pp. Englisch. …

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a nice introduction to Bayesian statistics with sufficient grounding in the Bayesian framework without being distracted by more esoteric pointsThe material is well-organized, weaving applications, background material and computation discu. …

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -and examples.- Belief, probability and exchangeability.- One-parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and Metropolis-Hastings algorithms.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch.…