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9780387922997 - A First Course in Bayesian Statistical Methods di Hoff, Peter D. (36 risultati)
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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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First Course in Bayesian Statistical Methods
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A First Course in Bayesian Statistical Methods
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A First Course in Bayesian Statistical Methods
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First Course in Bayesian Statistical Methods
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A FIRST COURSE IN BAYESIAN STATISTICAL M
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First Course in Bayesian Statistical Methods
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A First Course in Bayesian Statistical Methods (Hardcover)
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
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ISBN 10: 0387922997 ISBN 13: 9780387922997
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Hardcover. Condizione: new. Hardcover. * 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., US, 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
Hardback. Condizione: New. 1st ed. 2009. * 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.
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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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A First Course in Bayesian Statistical Methods
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Editore: Springer-Verlag New York Inc., US, 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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Aggiungi al carrelloHardback. Condizione: New. 1st ed. 2009. * 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.
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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First Course in Bayesian Statistical Methods
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A FIRST COURSE IN BAYESIAN STATISTICAL M
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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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A First Course in Bayesian Statistical Methods
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Aggiungi al carrelloGebunden. Condizione: New. 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.
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 271 | Sprache: Englisch | Produktart: Bücher | 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.
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., US, 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
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Hardback. Condizione: New. 1st ed. 2009. * 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.
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Aggiungi al carrelloBuch. 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.
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer US, New York, N.Y., 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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Aggiungi al carrelloBuch. Condizione: Neu. A First Course in Bayesian Statistical Methods | Peter D. Hoff | Buch | Springer Texts in Statistics | ix | Englisch | 2009 | Springer US, New York, N.Y. | EAN 9780387922997 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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A First Course in Bayesian Statistical Methods (Hardcover)
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., New York, NY, 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. * 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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A First Course in Bayesian Statistical Methods
Libro 37 di 111: Springer Texts in StatisticsLingua: Inglese
Editore: Springer-Verlag New York Inc., US, 2009
ISBN 10: 0387922997 ISBN 13: 9780387922997
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Aggiungi al carrelloHardback. Condizione: New. 1st ed. 2009. * 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.
