Bayesian Essentials with R PDF ePub eBook

Books Info:

Bayesian Essentials with R free pdf 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 Universite 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. Jean-Michel Marin is Professor of Statistics at Universite Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years. Christian Robert is Professor of Statistics at Universite Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015).

About Jean-Michel Marin

Jean-Michel Marin is Professor of Statistics at Universite Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years. Christian Robert is Professor of Statistics at Universite Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015).

Details Book

Author : Jean-Michel Marin
Publisher : Springer-Verlag New York Inc.
Data Published : 29 October 2013
ISBN : 1461486866
EAN : 9781461486862
Format Book : PDF, Epub, DOCx, TXT
Number of Pages : 296 pages
Age + : 15 years
Language : English
Rating :

Reviews Bayesian Essentials with R



17 Comments Add a comment




Related eBooks Download


  • Computational Bayesian Statistics free pdfComputational Bayesian Statistics

    There is a strong upsurge in the use of Bayesian methods in applied statistical analysis. yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter..


  • Doing Bayesian Data Analysis free pdfDoing Bayesian Data Analysis

    There is an explosion of interest in Bayesian statistics. primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis: A Tutorial with R..


  • Bayesian Models for Categorical Data free pdfBayesian Models for Categorical Data

    The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics. psychology. economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods..


  • Introduction to Bayesian Statistics free pdfIntroduction to Bayesian Statistics

    Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." -Statistics in Medical Research "[This book] is written in a lucid conversational style..


  • Intermediate Statistics and Econometrics free pdfIntermediate Statistics and Econometrics

    The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topic courses - giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances..


  • Bayesian Essentials with R free pdfBayesian Essentials with R

    . 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