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

By: Marin, Jean-Michel [author.]Contributor(s): Robert, Christian P, 1961- [author.] | Ohio Library and Information NetworkMaterial type: TextTextSeries: Springer texts in statisticsPublisher: New York : Springer, 2014Edition: Second edition; Second editionDescription: 1 online resource (xiv, 296 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9781461486879 (electronic bk.); 1461486874 (electronic bk.); 1461486866 (print); 9781461486862 (print)Subject(s): Bayesian statistical decision theory | R (Computer program language) | Statistics | Statistics and Computing/Statistics Programs | Statistical Theory and MethodsGenre/Form: Electronic books. Additional physical formats: Printed edition:: No titleDDC classification: 519.5/42 LOC classification: QA279.5Online resources: SpringerLink Connect to resource | SpringerLink Connect to resource | SpringerLink Connect to resource (off-campus)
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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e-Books e-Books Main Library -University of Zimbabwe
Click on Online resources to access the e-Book QA279.5 (Browse shelf (Opens below)) Available

Includes bibliographical references and index

User's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Series -- Image Analysis

Available to OhioLINK libraries

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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