Computational Bayesian Statistics
Computational Bayesian Statistics
Carlos Daniel Paulino, Universidade de Lisboa
Peter Müller, University of Texas, Austin
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.
- Presents a comparison of different software packages on the same data set gives hands-on knowledge of their relative strengths and limitations
- Contains problem sets which support readers in consolidating skills, building practical intuition, and extending the methods in the text to connect with the current literature
- Foregrounding of motivation and basic principles of inference helps readers to develop deeper and more intuitive understanding of statistical thinking, separate from mathematical formalism
Reviews & endorsements
‘An introduction to computational Bayesian statistics cooked to perfection, with the right mix of ingredients, from the spirited defense of the Bayesian approach, to the description of the tools of the Bayesian trade, to a definitely broad and very much up-to-date presentation of Monte Carlo and Laplace approximation methods, to a helpful description of the most common software. And spiced up with critical perspectives on some common practices and a healthy focus on model assessment and model selection. Highly recommended on the menu of Bayesian textbooks!' Christian Robert, Université de Paris IX (Paris-Dauphine) and University of Warwick
‘This book aims to be a concise introduction to modern computational Bayesian statistics, and it certainly succeeds! The authors carefully introduce every main technique that is around and demonstrate its use with the appropriate software. Additionally, the book contains a readable introduction to Bayesian methods, and brings the reader up to speed within the field in no time!' Håvard Rue, King Abdullah University of Science and Technology, Saudi Arabia
'Exercises are presented at the end of each chapter, with just over sixty such exercises in total - enough to provide some exposure to the practical problems which arise without being overwhelming. Overall the book is approachable and clearly written and numerous examples clarify abstract ideas as they arise.' Adam M. Johansen, Mathematical Reviews Clippings
'The authors of Computational Bayesian Statistics very wisely draw a line in the sand around the software and methodology associated with more traditional Bayesian statistical inference. The slender volume swiftly establishes Bayesian fundamentals, covers most of the more established and time‐proven inference methods, and eventually concludes with its unique selling point: a comprehensive treatment of various software packages, chiefly BUGS, JAGS, STAN, BayesX, and R‐INLA. … In this sense, the book acts as a powerful springboard for students to dive into the mighty deluge of Bayesian computational methods from our present-day position on the riverbank.' Biometrical Journal
Product details
April 2019Hardback
9781108481038
254 pages
235 × 156 × 17 mm
0.48kg
12 b/w illus.
Available
Table of Contents
- 1. Bayesian inference
- 2. Representation of prior information
- 3. Bayesian inference in basic problems
- 4. Inference by Monte Carlo methods
- 5. Model assessment
- 6. Markov chain Monte Carlo methods
- 7. Model selection and transdimensional MCMC
- 8. Methods based on analytic approximations
- 9. Software.
