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On computational tools for Bayesian data analysis

Robert, Christian P.; Marin, Jean-Michel (2010), On computational tools for Bayesian data analysis, in Böcker, Klaus, Rethinking Risk Measurement and Reporting, Risk Books : London

Type
Chapitre d'ouvrage
External document link
http://hal.archives-ouvertes.fr/hal-00473020/fr/
Date
2010
Book title
Rethinking Risk Measurement and Reporting
Book author
Böcker, Klaus
Publisher
Risk Books
Published in
London
ISBN
978-1-906348-40-3
Number of pages
527
Metadata
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Author(s)
Robert, Christian P.
Marin, Jean-Michel cc
Abstract (EN)
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential Monte Carlo methods and more recently Approximate Bayesian Computation techniques have considerably increased the potential for Bayesian applications and they have also opened new avenues for Bayesian inference, first and foremost Bayesian model choice.
Subjects / Keywords
latent variables models; Monte Carlo methods; Bayesian inference; adaptivity; Approximate Bayesian Computation techniques; MCMC algorithms; model choice
JEL
C15 - Statistical Simulation Methods: General
C11 - Bayesian Analysis: General