Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

23 Oct 2014Tim SalimansDiederik P. KingmaMax Welling

Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables. This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more steps of MCMC into our variational approximation... (read more)

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