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


Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on April 19, 2019 09:37 (2y ago)

What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.

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