Deep learning for Bayesian inference in intractable models

Stochastic models with intractable likelihood functions appear in many fields of science such as population genetics and spatial extremes analysis. Due to this intractability, traditional Bayesian methods such as Markov Chain Monte Carlo can be used to obtain samples from the posterior distribution of model parameters given observed data.…