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Bayesian Regression Modeling with INLA (Chapman & Hall/CRC Computer Science & Data Analysis) by Xiaofeng Wang; Yu Ryan Yue; Julian J. Faraway is a nonfiction available to read on EtoBox.

What is Bayesian Regression Modeling with INLA (Chapman & Hall/CRC Computer Science & Data Analysis) about?

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.

Who reads Bayesian Regression Modeling with INLA (Chapman & Hall/CRC Computer Science & Data Analysis)?

Author

Xiaofeng Wang; Yu Ryan Yue; Julian J. Faraway

Publisher

Taylor & Francis Group; Chapman and Hall/CRC

Published

2018

Language

EN

ISBN

9781351165747

Category

nonfiction

Subjects

Mathematics, Stem