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borderland which separates the known from the unknown”

-P.C.Mahalanobis


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

By: Contributor(s): Material type: TextTextSeries: Edition: Second editionDescription: xiv, 296 pages : illustrations (some color)ISBN:
  • 1461486866
  • 9781461486862
Subject(s): DDC classification:
  • 000SA.161 M337
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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.161 M337 (Browse shelf(Opens below)) Available 134962
Total holds: 0

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.

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