Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J. Bayesian regression modeling with INLA. (English) Zbl 1420.62005 Chapman & Hall/CRC Computer Science & Data Analysis Series. Boca Raton, FL: CRC Press (ISBN 978-1-4987-2725-9/hbk; 978-1-351-16575-4/ebook). xii, 312 p. (2018). INLA stands for integrated nested Laplace approximations. This method is used for fitting a broad class of Bayesian models. A very simple and over-examined model is the least square method. The extension of this method is a class of statistical models like GLM/GAM/GLMM/GAMM considered here. The INLA approach is not a rival/competitor/replacement to/of MCMC, just a better option of GLMs. R scripts for the class of INLA method are given at the web-page http://julianfaraway.github.io/brinla. Help for running the R scripts can be found here. It is a must-have book for everyone interested in the Bayesian regression method and INLA. Reviewer: Rózsa Horváth-Bokor (Budakalász) Cited in 2 ReviewsCited in 4 Documents MSC: 62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics 62J05 Linear regression; mixed models 62F15 Bayesian inference 62-04 Software, source code, etc. for problems pertaining to statistics Keywords:integrated nested Laplace approximations; Bayesian regression; GLM/GAM/GLMM/GAMM Software:R; R-INLA; MCMCglmm; Stan; brinla; JAGS; GitHub; BUGS PDFBibTeX XMLCite \textit{X. Wang} et al., Bayesian regression modeling with INLA. Boca Raton, FL: CRC Press (2018; Zbl 1420.62005) Full Text: Link