Bayesian Regression Modeling with INLA

Xiaofeng Wang, Yu Yue Ryan, Julian J. Faraway

February 22, 2018 by Chapman and Hall/CRC
Reference - 312 Pages
ISBN 9781498727259 - CAT# K25871
Series: Chapman & Hall/CRC Computer Science & Data Analysis


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  • Covers a variety of regression models
  • Discusses real case studies
  • Includes R code examples
  • Explains innovative and efficient Bayesian inference
  • Handles complex data


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.


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