INLA stands for Integrated Nested Laplace Approximations, which is a new
method for fitting a broad class of Bayesian regression models. 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. A
key theme throughout the book is that it makes sense to demonstrate the
interplay of theory and practice with reproducible studies. Complete R
commands are provided for each example, and a supporting website holds
all of the data described in the book. An R package including the data
and additional functions in the book is available to download.
The book is aimed at readers who have a basic knowledge of statistical
theory and Bayesian methodology. It gets readers up to date on the
latest in Bayesian inference using INLA and prepares them for
sophisticated, real-world work.
Xiaofeng Wang is Professor of Medicine and Biostatistics at the
Cleveland Clinic Lerner College of Medicine of Case Western Reserve
University and a Full Staff in the Department of Quantitative Health
Sciences at Cleveland Clinic.
Yu Ryan Yue is Associate Professor of Statistics in the Paul H.
Chook Department of Information Systems and Statistics at Baruch
College, The City University of New York.
Julian J. Faraway is Professor of Statistics in the Department of
Mathematical Sciences at the University of Bath.
