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Original Essays | September 30, 2014

Benjamin Parzybok: IMG A Brief History of Video Games Played by Mayors, Presidents, and Emperors



Brandon Bartlett, the fictional mayor of Portland in my novel Sherwood Nation, is addicted to playing video games. In a city he's all but lost... Continue »
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A First Course in Bayesian Statistical Methods

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A First Course in Bayesian Statistical Methods 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:
9781441928283
Author:
Hoff, Peter D.
Publisher:
Springer
Author:
Springer
Location:
New York, NY
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:
Operation Research/Decision Theory
Subject:
The Arts
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Subject:
Social sciences_xMethodology
Subject:
Computer Science
Subject:
Operations Research
Subject:
Data mining
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2009
Series:
Springer Texts in Statistics
Publication Date:
20101119
Binding:
TRADE PAPER
Language:
English
Pages:
282
Dimensions:
235 x 155 mm 419 gr

Related Subjects

History and Social Science » Economics » General
History and Social Science » Sociology » Reference and Methodology
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 New Trade Paper
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$77.50 In Stock
Product details 282 pages Springer - English 9781441928283 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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