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.
General
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