Incorporating Expressive Graphical Models in Variational
Approximations: Chain-Graphs and Hidden Variables
E. El-Hay and N. Friedman
In Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence
(UAI), 2001.
Postscript version
PDF version.
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.
Back to Nir's publications page
nir@cs.huji.ac.il