Bayesian Regression Modeling with INLA Wang Xiaofeng Cleveland Clinic Lerner College of Medicine Case Western Reserve University USAPevná vazba

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference.Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity.

An R package including the data and additional functions in the book is available to Complete R commands are provided for each example, and a supporting website holds all of the data described in the book.