Overview
- The perfect entry for gaining a practical understanding of Bayesian methodology
- Guides the reader into the practice of prior modeling and Bayesian computing for the most classical models
- Computational aspects are sufficiently detailed to achieve effective programming of the methods with little effort
- Datasets, R codes and course slides are available on the book website
- Includes supplementary material: sn.pub/extras
- Request lecturer material: sn.pub/lecturer-material
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (8 chapters)
Keywords
About this book
Reviews
From the reviews:
"The matching of each computational technique to a real data set allows readers to fully appreciate the Bayesian analysis process, from model formation to prior selection and practical implementation." (Lawrence Joseph from Biometrics, Issue 63, September 2007)
"Recent times have seen several new books introducing Bayesian computing. This book is an introduction on a higher level. ‘The purpose of this book is to provide a self-contained entry to practical & computational Bayesian Statistics using generic examples from the most common models.’ … Many researchers and Ph.D. students will find the R-programs in the book a nice start for their own problems and an innovative source for further developments." (Wolfgang Polasek, Statistical Papers, Vol. 49, 2008)
"This text intentionally focuses on a few fundamental Bayesian statistical models and key computational tools. … Bayesian Core is more than a textbook: it is an entire course carefully crafted with the student in mind. … As an instructor of Bayesian statistics courses, I was pleased to discover this ready- and well-made, self-contained introductory course for (primarily) graduate students in statistics and other quantitative disciplines. I am seriously considering Bayesian Core for my next course in Bayesian statistics." (Jarrett J. Barber, Journal of the American Statistical Association, Vol. 103 (481), 2008)
"The book aims to be a self-contained entry to Bayesian computational statistics for practitioners as well as students at both the graduate and undergraduate level, and has been test-driven in a number of courses given by the authors. … Two particularly attractive aspects of the book are its concise and clear writing style, which is really enjoyable, and its focus on the development of an intuitive feel for the material: the numerous insightful remarks should make the book a real treat … ." (Pieter Bastiaan Ober, Journal of Applied Statistics, Vol. 35 (1),2008)
"The book is a good, compact and self-contained introduction to the applications of Bayesian statistics and to the use of R to implement the procedures. … a reader with a previous formal course in statistics will enjoy reading this book. … the authors are not shy of presenting such complex models as hidden Markov models and Markov random fields in a simple and direct way. This adds an edge to a compact and useful text." (Mauro Gasparini, Zentralblatt MATH, Vol. 1137 (15), 2008)
"This book’s title captures its focus. It is a textbook covering the core statistical models from both a Bayesian viewpoint and a computational viewpoint. … There is a discussion of choice of priors, along with math to derive the priors. … The book is being actively used as a textbook by a number of university courses. … The course level is graduate or advanced undergraduate. Solutions to the exercises are available to course instructors … . In conclusion, the book does what it does, well." (Rohan Baxter, ACM Computing Reviews, December, 2008)
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Core: A Practical Approach to Computational Bayesian Statistics
Authors: Jean-Michel Marin, Christian P. Robert
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-0-387-38983-7
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag New York 2007
eBook ISBN: 978-0-387-38983-7Published: 26 May 2007
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XIV, 258
Number of Illustrations: 80 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods, Probability and Statistics in Computer Science, Simulation and Modeling, Computational Intelligence, Computational Mathematics and Numerical Analysis