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Bayesian Core: A Practical Approach to Computational Bayesian Statistics: A Practical Approach to Computational Bayesian Statistics

Bayesian Core: A Practical Approach to Computational Bayesian Statistics: A Practical Approach to Computational Bayesian Statistics

          
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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. 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.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. 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.

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About the Book

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. 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. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language. The Bayesian Core can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at University Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). analyzing data the Bayesian way as well as an introductory course on Bayesian statistics. The prerequisites for the book are a basic knowledge of probability theory and of statistics. Methodological and data-based exercises are included within the main text and students are expected to solve them as they read the book. Those exercises can obviously serve as assignments, as was done in the above courses. Datasets, R codes and course slides all are available on the book website.


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Product Details
  • ISBN-13: 9780387389790
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Depth: 19
  • Language: English
  • Returnable: N
  • Spine Width: 23 mm
  • Weight: 476 gr
  • ISBN-10: 0387389792
  • Publisher Date: 08 Nov 2018
  • Binding: Hardback
  • Height: 241 mm
  • No of Pages: 255
  • Series Title: Springer Texts in Statistics
  • Sub Title: A Practical Approach to Computational Bayesian Statistics
  • Width: 159 mm


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