36:33
publiée le 27 novembre 2023
Monte Carlo guided Diffusion for Bayesian linear inverse problems
De Sylvain Le Corff
Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1
Apparaît dans la collection : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmes
Approximate Bayesian computation (ABC) techniques, also known as likelihood-free methods, have become a standard tool for the analysis of complex models, primarily in population genetics. The development of new ABC methodologies is undergoing a rapid increase in the past years, as shown by multiple publications, conferences and softwares. In this lecture, we introduce some recent advances on ABC techniques, notably for model choice problems.
