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December 2013 Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Tim Salimans, David A. Knowles
Bayesian Anal. 8(4): 837-882 (December 2013). DOI: 10.1214/13-BA858


We propose a general algorithm for approximating nonstandard Bayesian posterior distributions. The algorithm minimizes the Kullback-Leibler divergence of an approximating distribution to the intractable posterior distribution. Our method can be used to approximate any posterior distribution, provided that it is given in closed form up to the proportionality constant. The approximation can be any distribution in the exponential family or any mixture of such distributions, which means that it can be made arbitrarily precise. Several examples illustrate the speed and accuracy of our approximation method in practice.


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Tim Salimans. David A. Knowles. "Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression." Bayesian Anal. 8 (4) 837 - 882, December 2013.


Published: December 2013
First available in Project Euclid: 4 December 2013

zbMATH: 1329.62142
MathSciNet: MR3150471
Digital Object Identifier: 10.1214/13-BA858

Keywords: approximate inference , stochastic approximation , variational Bayes

Rights: Copyright © 2013 International Society for Bayesian Analysis


Vol.8 • No. 4 • December 2013
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