PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Expectation Propagation on the Maximum of Correlated Normal Variables
Philipp Hennig
Technical Report, arXiv Volume Stat:ML, Number 0910.0115, 2009.

Abstract

Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corresponding to Gaussian approximations minimizing relative entropy). It is shown how this can be used to build a heuristic approximation to the maximum relationship over a finite set of Gaussian variables, allowing approximate inference by Expectation Propagation on such quantities.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5769
Deposited By:Philipp Hennig
Deposited On:08 March 2010