A Filtering Approach to Stochastic Variational Inference

Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data. We present an alternative perspective on SVI as approximate parallel coordinate ascent. SVI trades-off bias and variance to step close to the unknown true coordinate optimum given by batch variational Bayes (VB). We define a model to automate this process. As a consequence of this construction, we update the variational parameters using Bayes rule, rather than a hand-crafted optimization schedule.