PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables
Tal El-Hay and Nir Friedman
Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI) 2001.

Abstract

Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model determines a tradeoff between the complexity of the approximation procedure and the quality of the approximation. In this paper, we consider variational approximations based on two classes of models that are richer than standard Bayesian networks, Markov networks or mixture models. As such, these classes allow to find better tradeoffs in the spectrum of approximations. The first class of models are chain graphs, which capture distributions that are partially directed. The second class of models are directed graphs (Bayesian networks) with additional latent variables. Both classes allow representation of multi-variable dependencies that cannot be easily represented within a Bayesian network.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7049
Deposited By:Tal El-Hay
Deposited On:24 February 2011