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A First Course in Bayesian Statistical Methods

  • Peter D.¬†Hoff

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Peter D. Hoff
    Pages 1-12
  3. Peter D. Hoff
    Pages 13-30
  4. Peter D. Hoff
    Pages 31-52
  5. Peter D. Hoff
    Pages 53-65
  6. Peter D. Hoff
    Pages 67-87
  7. Peter D. Hoff
    Pages 105-123
  8. Peter D. Hoff
    Pages 125-147
  9. Peter D. Hoff
    Pages 149-170
  10. Peter D. Hoff
    Pages 209-223
  11. Back Matter
    Pages 225-270

About this book

Introduction

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.

Keywords

Markov chain Statistical Computing Statistical Method Statistical Models algorithms data analysis linear regression modeling

Authors and affiliations

  • Peter D.¬†Hoff
    • 1
  1. 1.Department of StatisticsUniversity of WashingtonSeattleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-92407-6
  • Copyright Information Springer-Verlag New York 2009
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-92299-7
  • Online ISBN 978-0-387-92407-6
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • Buy this book on publisher's site