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We introduce a new algorithm for approximate inference that combinesreparametrization, Markov chain Monte Carlo and variational methods. Weconstruct a very flexible implicit variational distribution synthesized by anarbitrary Markov chain Monte Carlo operation and a deterministic transformationthat can be optimized using the reparametrization trick. Unlike current methodsfor implicit variational inference, our method avoids the computation of logdensity ratios and therefore it is easily applicable to arbitrary continuousand differentiable models. We demonstrate the proposed algorithm for fittingbanana-shaped distributions and for training variational autoencoders.

Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions