Variational Inference and Monte Carlo Sampling are currently the two chief ways of doing approximate Bayesian inference. In the Bayesian setting, we typically have some observed variables \(x\) and unobserved variables \(z\), and our goal is to calculate \(P(z|x)\). In all but the simplest cases, calculating \(P(z …