Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these are amenable to automation and allow online, scalable, universal approximate Bayesian inference. Unfortunately, common Probabilistic Programming Languages (PPLs) with stochastic engines lack efficiency of message passing-based algorithms deterministic...