Sign-Ons


Seminars: Dept Seminar


ABC as in inference, the empirical likelihood illustration

Date: Monday, October 29
Time: 4:10 pm -- 5:00 pm
Place: Snedecor 3105
Speaker: Christian Robert, Ceremade - Université Paris-Dauphine, France

Abstract:

Zyskind Lecture


 Approximate Bayesian computation (ABC) has now become an essential tool for the analysis of complex stochastic models when the likelihood function is unavailable. The well-established statistical method of empirical likelihood however provides another route to such settings that bypasses simulations from the model and the choices of the ABC parameters (summary statistics, distance, tolerance), while being provably convergent in the number of observations. Furthermore, avoiding model simulations leads to significant time savings in complex models, as those used in population genetics. The ABCel algorithm we present in this talk provides in addition an evaluation of its own performances through an associated effective sample size. (Joint work with Kerrie Mengersen and Pierre Pudlo.)