Bayesian regression modeling with INLA

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...

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Bibliographic Details
Main Authors: Wang, Xiaofeng (Author), Yue, Yu (Author), Faraway, Julian James (Author)
Format: Book
Language:English
Published: Boca Raton, FL CRC Press, Taylor & Francis Group 2018
Series:Chapman & Hall/CRC computer science and data analysis series
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245 1 0 |a Bayesian regression modeling with INLA  |c Xiaofeng Wang Yu Ryan Yue, Julian J. Faraway. 
246 3 |a Bayesian regression modeling with integrated Laplace approximation 
264 1 |a Boca Raton, FL  |b CRC Press, Taylor & Francis Group  |c 2018 
300 |a xii, 312 pages  |b illustrations  |c 24 cm. 
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490 1 |a Chapman & Hall/CRC computer science and data analysis series 
504 |a Includes bibliographical references and index. 
520 |a 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. 
592 |a 37584  |b 07/08/2019  |c RM 353.40  |h Bookline 
650 0 |a Regression analysis. 
650 0 |a Bayesian statistical decision theory. 
650 0 |a Laplace transformation. 
700 1 |a Yue, Yu,  |e author. 
700 1 |a Faraway, Julian James,  |e author. 
830 0 |a Chapman & Hall/CRC computer science and data analysis series 
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