Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions

4 Aug 2017Michalis K. Titsias

We introduce a new algorithm for approximate inference that combines reparametrization, Markov chain Monte Carlo and variational methods. We construct a very flexible implicit variational distribution synthesized by an arbitrary Markov chain Monte Carlo operation and a deterministic transformation that can be optimized using the reparametrization trick... (read more)

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