I am writing a book entitled Bayesian Regression with INLA with Xiaofeng Wang and Ryan Yue. INLA stands for integrated nested Laplace approximations. Bayesian computation is not straightforward. In a few simple cases, explicit solutions exist, but in most statistical applications one typically uses simulation – usually based on MCMC (Markov chain Monte Carlo) methods. In some cases, this simulation can take a long time so it would be nice if you could do it faster. INLA is an approximation-based method that can do some Bayesian model fitting computation very quickly compared to simulation-based methods. You can learn more at the R-INLA website. You can also see some preview examples from the book.