In our quest for understanding generative models in Machine Learning, we started by learning Statistics for ML Engineers, then we looked at Bayesian Linear Regression and the Exponential Family of Distributions to learn how to compute the Maximum Likelihood (MLE) and Maximum a Posteriori (MAP) estimators on parametric distributions. We then looked into Variational Inference as a method to perform generative ML on non-parametric or computationally-intractable Bayesian formulations. In this post, ...