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22 Oct 2015 24 Oct 2017 Video created by National Research University Higher School of Economics for the course "Bayesian Methods for Machine Learning". can show that the EM algorithm can be viewed as a special case of this methodology. In this article, we first present a tutorial introduction of Bayesian variationalIn this paper we first present a tutorial introduction of Bayesian variational inference Bayesian variational inference offers as compared to the EM algorithm. 1. Abstract This tutorial describes the mean-field variational Bayesian approximation to inference in .. These updates give us an EM-like algorithm, optimising. 26 Feb 2017 There are many great tutorials for variational inference, but I found the tutorial by Tzikas et al. to be the most helpful. It follows the steps of 7 Aug 2016 This post is an introductory tutorial on Variational Methods. I will derive the optimization objective for the simplest of VB methods, known as the Variational Inference. David M. Blei. 1 Set up. • As usual, we will assume that x = x1:n are observations and z = z1:m are hidden variables. We assume additional defining the F(q, ?) functional, which is a lower bound on the log likelihood. In the EM algorithm, we alternately optimize F(q, ?) wrt q and ?, and we can prove.