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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary rand…
On Using Control Variates with Stochastic Approximation for Variational Bayes and its Connection to Stochastic Linear Regression
Tim Salimans, David A. Knowles
Recently, we and several other authors have written about the possibilities of using stochastic approximation techniques for fitting variational approximations to intractable Bayes…
Implementing and Automating Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Tim Salimans
We recently proposed a general algorithm for approximating nonstandard Bayesian posterior distributions by minimization of their Kullback-Leibler divergence with respect to a more…
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Tim Salimans, David A. Knowles
We propose a general algorithm for approximating nonstandard Bayesian posterior distributions. The algorithm minimizes the Kullback-Leibler divergence of an approximating distribut…