Bayesian Regression Modeling with INLA

Julian Faraway, Xiaofeng Wang, Yu Yue

Research output: Book/ReportBook

Abstract

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.
Original languageEnglish
PublisherChapman & Hall
Number of pages304
ISBN (Print)9781498727259
Publication statusPublished - 30 Jan 2018

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Laplace Approximation
Regression
Laplace's Method
Markov Chain Monte Carlo Algorithms
Bayesian Model
Bayesian inference
Marginal Distribution
Posterior distribution
Modeling
Approximation Methods
Regression Model
Data analysis
Alternatives
Approximation
Range of data
Framework

Cite this

Faraway, J., Wang, X., & Yue, Y. (2018). Bayesian Regression Modeling with INLA. Chapman & Hall.

Bayesian Regression Modeling with INLA. / Faraway, Julian; Wang, Xiaofeng; Yue, Yu.

Chapman & Hall, 2018. 304 p.

Research output: Book/ReportBook

Faraway, J, Wang, X & Yue, Y 2018, Bayesian Regression Modeling with INLA. Chapman & Hall.
Faraway J, Wang X, Yue Y. Bayesian Regression Modeling with INLA. Chapman & Hall, 2018. 304 p.
Faraway, Julian ; Wang, Xiaofeng ; Yue, Yu. / Bayesian Regression Modeling with INLA. Chapman & Hall, 2018. 304 p.
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