PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian networks on Dirichlet distributed vectors
Wray Buntine, Lan Du and Petteri Nurmi
In: Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM-10), Helsinki, Finland(2010).


Exact Bayesian network inference exists for Gaussian and multinomial distributions. For other kinds of distributions, approximations or restrictions on the kind of inference done are needed. In this paper we present generalized networks of Dirichlet distributions, and show how, using the two-parameter Poisson-Dirichlet distribution and Gibbs sampling, one can do approximate inference over them. This involves integrating out the probability vectors but leaving auxiliary discrete count vectors in their place. We illustrate the technique by extending standard topic models to "structured" documents, where the document structure is given by a Bayesian network of Dirichlets.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:7342
Deposited By:Wray Buntine
Deposited On:17 March 2011