# Variational Inference Guide

Neil D. Lawrence, University of Sheffield

#### Abstract

This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.

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 %T Variational Inference Guide %A Neil D. Lawrence %B %D %F lawrence-variationalguide02 %P -- %R %U http://inverseprobability.com/publications/lawrence-variationalguide02.html %X This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach. 
 TY - CPAPER TI - Variational Inference Guide AU - Neil D. Lawrence PY - 2002/01/01 DA - 2002/01/01 ID - lawrence-variationalguide02 SP - EP - L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/variationalInference.pdf UR - http://inverseprobability.com/publications/lawrence-variationalguide02.html AB - This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach. ER - 
 Lawrence, N.D.. (2002). Variational Inference Guide.:-