Jean-Michel Marin - Some recent advances on Approximate Bayesian Computation techniques

In an increasing number of application domains, the statistical model is so complex that the point-wise computation of the likelihood is intractable. That is typically the case when the underlying probability distribution involves numerous latent variables. Approximate Bayesian Computation (ABC) is a widely used technique to bypass that difficulty. I will review some recent developments on ABC techniques, emphazing the fact that modern machine learning approaches are useful in this field. Although intrinsically very different of PAC-Bayesian strategies - the choice of a generative model is essential within the ABC paradigm - I will highlight some links between these two methodologies.