Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps

In the Markov chain Monte Carlo methods have become recent years, the evolution of deep neural networks has notably popular in statistics as versatile techniques to sample propelled the field of Variational Inference (Rezende & from complicated probability distributions. In Mohamed, 2015; Kingma & Welling, 2014; Rezende et al., this work, we propose a method to parameterize 2014; Kingma et al., 2016) whilst MCMC methods have and train transition kernels of Markov chains not benefited much from these advances. Using neural networks to achieve efficient sampling and good mixing.