Bayesian Regression Modeling with INLA

<p>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.</p><p>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.</p><p></p><p>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.</p><p></p><p><strong>Xiaofeng Wang</strong> 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.</p><p></p><p><strong>Yu Ryan Yue</strong> 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.</p><p></p><p><strong>Julian J. Faraway</strong> is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.</p><p></p>