** en vidéoconférence à la salle S-116, Pavillon Roger-Gaudry, Université de Montréal
Approximate Bayesian computation (ABC) has now become an essential tool for the analysis of complex stochastic models when the likelihood function is unavailable. The approximation is seen as a nuisance from a computational statistic point of view but we argue here it is also a blessing from an inferential perspective. We illustrate this paradoxical stand in the case of dynamic models and population genetics models. There are also major inference difficulties, as detailed in the case of Bayesian model choice.