A First Course in Bayesian Statistical Methods [electronic resource] / by Peter D. Hoff.

By: Hoff, Peter D [author.]
Contributor(s): SpringerLink (Online service)
Material type: TextTextSeries: Springer Texts in Statistics: Publisher: New York, NY : Springer New York, 2009Description: X, 272 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387924076Subject(s): Mathematics | Mathematical statistics | Operations research | Management science | Probabilities | Statistics | Econometrics | Social sciences | Mathematics | Probability Theory and Stochastic Processes | Operations Research, Management Science | Statistical Theory and Methods | Methodology of the Social Sciences | Probability and Statistics in Computer Science | EconometricsAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
Item type Current location Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library

Electronic Book@IST

EBook Available
Total holds: 0

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.

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.

There are no comments for this item.

to post a comment.

Powered by Koha