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Bayesian Regression Modeling with INLA

January 22, 2019
Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway ... 324 pages - Publisher: Chapman and Hall/CRC; Language: English - ISBN-10: 1498727255 - ISBN-13: 978-1498727259 ...

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.

Bayesian Regression Modeling with INLA, Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway


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