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Bayesian regression modeling with INLA

By: Wang, Xiaofeng.
Contributor(s): Yue, Yu Ryan [Co author] | Faraway, Julian J [Co author].
Material type: materialTypeLabelBookSeries: Computer science and data analysis series. Publisher: Florida CRC Press 2018Description: xii, 312p. With index.ISBN: 9781498727259.Subject(s): Regression analysis | Bayesian statistical decision theory | Gaussian processes | Laplace transformationDDC classification: 519.542 Summary: 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. https://www.crcpress.com/Bayesian-Regression-Modeling-with-INLA/Wang-Ryan-Faraway/p/book/9781498727259
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Table of Contents
1.Introduction

2.Theory of INLA

3.Bayesian Linear Regression

4.Generalized Linear Models

5.Linear Mixed and Generalized Linear Mixed Models

6.Survival Analysis

7.Random Walk Models for Smoothing Methods

8.Gaussian Process Regression

9.Additive and Generalized Additive Models

10.Errors-in-Variables Regression

11.Miscellaneous Topics in INLA

Appendix A Installation
Appendix B Uninformative Priors in Linear Regression

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.

https://www.crcpress.com/Bayesian-Regression-Modeling-with-INLA/Wang-Ryan-Faraway/p/book/9781498727259

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