Bayesian Regression Modeling with INLA

1st Edition

Xiaofeng Wang, Yu Yue Ryan, Julian J. Faraway

Chapman and Hall/CRC
Published February 16, 2018
Reference - 312 Pages
ISBN 9781498727259 - CAT# K25871
Series: Chapman & Hall/CRC Computer Science & Data Analysis

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Features

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

Summary

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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