- Publisher: Cambridge University Press
- Online publication date: February 2019
- Print publication year: 2019
- Online ISBN: 9781108646185
- DOI: https://doi.org/10.1017/9781108646185
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.
Christian Robert - Université de Paris IX, Paris-Dauphine, and University of Warwick
Håvard Rue - King Abdullah University of Science and Technology, Saudi Arabia
Adam M. Johansen Source: Mathematical Reviews Clippings
Source: Biometrical Journal
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