Bayesian essentials with R / Jean-Michel Marin, Christian P. Robert.

By: Contributor(s): Material type: TextTextSeries: Springer texts in statisticsPublisher: New York : Springer, [2014]Edition: Second editionDescription: xiv, 296 pages : illustrations (some color) ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781461486862 (hbk)
  • 1461486866 (hbk)
Subject(s): Additional physical formats: Available online as:: Bayesian essentials with R.LOC classification:
  • QA279.5 .M358 2014
Online resources: Available additional physical forms:
  • Also available online.
Contents:
User's manual -- Normal models -- Regression and variable selection -- Generalized linear models -- Capture-recapture experiments -- Mixture models -- Time series -- Image analysis.
Summary: 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.
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Includes bibliographical references (pages 287-290) and index.

User's manual -- Normal models -- Regression and variable selection -- Generalized linear models -- Capture-recapture experiments -- Mixture models -- Time series -- Image analysis.

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.

Also available online.

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