3.3 Importance sampling methods for Bayesian discrimination between embedded models (Jean-Michel Marin)


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Manage episode 188707051 series 1600644
Par Universite Paris 1 Pantheon-Sorbonne, découvert par Player FM et notre communauté - Le copyright est détenu par l'éditeur, non par Player F, et l'audio est diffusé directement depuis ses serveurs. Appuyiez sur le bouton S'Abonner pour suivre les mises à jour sur Player FM, ou collez l'URL du flux dans d'autre applications de podcasts.
We survey some approaches on the approximation of Bayes factors used in Bayesian model choice and propose a new one. 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, MLE based importance sampling, bridge and harmonic mean sampling, Chib?s method based on the exploitation of a functional equality, as well as a revisited Savage-Dickey?s approximation. We demonstrate in this survey how all these 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. This is a joint work with Christian P. Robert.

12 episodes