PyData DC 2016
Inference in Bayesian models often requires simulation to approximate posterior distributions. Variational inference (VI) instead approximates posteriors through optimization. Recent theoretical and computational advances in automatic variational inference have made VI more accessible. This talk will give an introduction to VI and show software packages for performing VI in Python.