Conference paper
Approximate Inference in Probabilistic Models
In
Lecture Notes in Computer Science
—
2004, pp. 494-504
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. The abstract should summarize the contents of the paper using at least 70 and at most 150 words. It will be set in 9-point font size and be inset 1.0 cm from the right and left margins.
There will be two blank lines before and after the Abstract.
| Language: | English |
|---|---|
| Publisher: | Springer |
| Year: | 2004 |
| Pages: | 494-504 |
| Proceedings: | Annual International Conference on Algorithmic Learning Theory |
| Series: | Lecture Notes in Computer Science |
| ISBN: | 3540233563 , 3540302158 , 9783540233565 and 9783540302155 |
| ISSN: | 03029743 |
| Types: | Conference paper |
| ORCIDs: | Winther, Ole |
