Bayesian Essentials with R

  • Jean-Michel Marin
  • Christian P. Robert

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

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Jean-Michel Marin, Christian P. Robert
    Pages 1-23
  3. Jean-Michel Marin, Christian P. Robert
    Pages 25-64
  4. Jean-Michel Marin, Christian P. Robert
    Pages 65-101
  5. Jean-Michel Marin, Christian P. Robert
    Pages 103-138
  6. Jean-Michel Marin, Christian P. Robert
    Pages 139-171
  7. Jean-Michel Marin, Christian P. Robert
    Pages 173-207
  8. Jean-Michel Marin, Christian P. Robert
    Pages 209-250
  9. Jean-Michel Marin, Christian P. Robert
    Pages 251-283
  10. Back Matter
    Pages 285-296

About this book

Introduction

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis.

This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics. 

Keywords

Bayesian R Bayesian data analysis Bayesian methodology Bayesian modeling Bayesian textbook Computational Statistics R

Authors and affiliations

  • Jean-Michel Marin
    • 1
  • Christian P. Robert
    • 2
  1. 1.Université Montpellier 2Montpellier cedex 5France
  2. 2.CeremadeUniversité Paris-DauphineParis cedex 16France

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-8687-9
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-8686-2
  • Online ISBN 978-1-4614-8687-9
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • About this book