Xiaofeng Wang, Cleveland Clinic, Cleveland, Ohio ; Yu Yue, Baruch College, the City University of New York ; Julian J. Faraway, University of Bath, UK.
Chapman & Hall/CRC Computer Science & Data Analysis
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
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.
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SUMMARY OR ABSTRACT
Text of Note
"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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Ingram Content Group
Stock Number
9781351165747
OTHER EDITION IN ANOTHER MEDIUM
Title
Bayesian regression modeling with INLA.
International Standard Book Number
9781498727259
PARALLEL TITLE PROPER
Parallel Title
Bayesian regression modeling with integrated Laplace approximation