Importance sampling methods for Bayesian discrimination between embedded models - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2010

Importance sampling methods for Bayesian discrimination between embedded models

Résumé

This paper surveys some well-established approaches on the approximation of Bayes factors used in Bayesian model choice, mostly as covered in Chen et al. (2000). Our focus here is on methods that are based on importance sampling strategies rather than variable dimension techniques like reversible jump MCMC, including: crude Monte Carlo, maximum likelihood based importance sampling, bridge and harmonic mean sampling, as well as Chib's method based on the exploitation of a functional equality. We demonstrate in this survey how these different methods can be efficiently implemented for testing the significance of a predictive variable in a probit model. Finally, we compare their performances on a real dataset.

Dates et versions

hal-00424475 , version 1 (16-10-2009)

Identifiants

Citer

Jean-Michel Marin, Christian Robert. Importance sampling methods for Bayesian discrimination between embedded models. M.-H. Chen, D. Dey, P. Mueller, D. Sun and K. Ye. Frontiers of Statistical Decision Making and Bayesian Analysis, Springer-Verlag, New York, pp.513-527, 2010. ⟨hal-00424475⟩
227 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More