PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate Inference in Probabilistic Models
Manfred Opper and Ole Winther
In: Algorithmic Learning Theory Lecture Notes in Artificial Intelligence (3244). (2004) Springer , Berlin , pp. 494-504. ISBN 3-540-23356-3

Abstract

We present a framework for approximate inference in probabilistic data models which is based on free energies. The free energy is constructed from two approximating distributions which encode different aspects of the intractable model. Consistency between distributions is required on a chosen set of moments. We find good performance using sets of moments which either specify factorized nodes or a spanning tree on the nodes.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:809
Deposited By:Manfred Opper
Deposited On:01 January 2005