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This title in other editions

A First Course in Bayesian Statistical Methods (Statistics for Social and Behavioral Sciences)

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A First Course in Bayesian Statistical Methods (Statistics for Social and Behavioral Sciences) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

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.

Synopsis:

  1. A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material.

  2. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.

  3. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Synopsis:

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.

Table of Contents

Introduction 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 the Metropolis-Hastings algorithm.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.

Product Details

ISBN:
9780387922997
Author:
Hoff, Peter D.
Publisher:
Springer
Author:
Cram101 Textbook Reviews
Location:
New York, NY
Subject:
Probability & Statistics - General
Subject:
Computer Science
Subject:
Database Management - Database Mining
Subject:
Probability & Statistics - Bayesian Analysis
Subject:
Statistics
Subject:
MCMC
Subject:
exchangeability
Subject:
hierarchical model
Subject:
Prediction
Subject:
variable selection
Subject:
Statistical Theory and Methods
Subject:
Methodology of the Social Sciences
Subject:
Probability and Statistics in Computer Science
Subject:
Econometrics
Subject:
Operations Research/Decision Theory
Subject:
Data Mining and Knowledge Discovery
Subject:
Mathematics - General
Subject:
Education-General
Subject:
Operation Research/Decision Theory
Subject:
The Arts
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Subject:
Social sciences_xMethodology
Subject:
Operations Research
Subject:
Data mining
Copyright:
Edition Description:
1st ed. 2009
Series:
Springer Texts in Statistics
Publication Date:
20090615
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
282
Dimensions:
235 x 155 mm 1270 gr

Related Subjects

Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

A First Course in Bayesian Statistical Methods (Statistics for Social and Behavioral Sciences) New Hardcover
0 stars - 0 reviews
$77.50 In Stock
Product details 282 pages Springer - English 9780387922997 Reviews:
"Synopsis" by , 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.
"Synopsis" by ,
  1. A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material.

  2. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.

  3. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

"Synopsis" by , 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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