INLA stands for Integrated Nested Laplace Approximations. It is used for fitting Latent Gaussian models (LGM). LGMs include a wide range of commonly used regression models. Unlike MCMC which uses simulation methods, INLA uses approximation methods for Bayesian model fitting. Within the class of LGMs, INLA can fit models much faster than MCMC-based methods.
The brinla R package contains data and functions to support the book.
brinla package with:
Here are the errata. If you find any other errata, please let us know according to the chapter: Ch3, 4, 6, 10 (Xiaofeng Wang), Ch2, 7 or 9 (Ryan Yue) or Ch1, 5 or 8 (Julian Faraway).
Here are some examples
The book can be purchased at the usual online outlets or the publishers