Bayesian regression modeling with INLA / Xiaofeng Wang, Cleveland Clinic, Cleveland, Ohio, Yu Ryan Yue, Baruch College, The City University of New York, Julian J. Faraway, University of Bath, UK
Introduction -- Theory of INLA -- Bayesian linear regression -- Generalized linear models -- Linear mixed and generalized linear mixed models -- Survival analysis -- Random walk models for smoothing methods -- Gaussian process regression -- Additive and generalized additive models -- Errors-in-variables regression -- Miscellaneous topics in INLA
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."--Provided by publisher
Bibliography
Includes bibliographical references (pages 297-308) and index
Notes
Description based on print version record; title from PDF title page (viewed August 29, 2022)
