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 p… Mehr…
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., Springer<
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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… Mehr…
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.; PDF; Scientific, Technical and Medical > Mathematics > Probability & statistics, Springer New York<
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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 p… Mehr…
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., Springer<
Nr. 978-0-387-92407-6. Versandkosten:Worldwide free shipping, , DE. (EUR 0.00)
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… Mehr…
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.; PDF; Scientific, Technical and Medical > Mathematics > Probability & statistics, Springer New York<
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A First Course in Bayesian Statistical Methods: ab 53.49 € eBooks > Fachthemen & Wissenschaft > Sozialwissenschaften Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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Buch in der Datenbank seit 2011-11-04T20:59:26+01:00 (Berlin) Detailseite zuletzt geändert am 2024-01-11T11:34:27+01:00 (Berlin) ISBN/EAN: 0387924078
ISBN - alternative Schreibweisen: 0-387-92407-8, 978-0-387-92407-6 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: peter hoff, syd hoff Titel des Buches: statistical methods, first course bayesian, new bayesian, bayes
Daten vom Verlag:
Autor/in: Peter D. Hoff Titel: Springer Texts in Statistics; A First Course in Bayesian Statistical Methods Verlag: Springer; Springer US 271 Seiten Erscheinungsjahr: 2009-06-02 New York; NY; US Gedruckt / Hergestellt in Großbritannien. Sprache: Englisch 53,49 € (DE) 55,00 € (AT) 59,00 CHF (CH) Available X, 272 p.
EA; E107; eBook; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Markov chain; Statistical Computing; Statistical Method; Statistical Models; algorithms; data analysis; linear regression; modeling; A; Probability Theory and Stochastic Processes; Operations Research, Management Science; Statistical Theory and Methods; Methodology of the Social Sciences; Probability and Statistics in Computer Science; Econometrics; Probability Theory; Operations Research, Management Science; Statistical Theory and Methods; Sociological Methods; Probability and Statistics in Computer Science; Econometrics; Mathematics and Statistics; Stochastik; Unternehmensforschung; Soziologie; Mathematik für Informatiker; Ökonometrie und Wirtschaftsstatistik; BB
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.
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