Approximate Inference in Probabilistic Models |
Manfred Opper, Ole Winther
|
| Abstract | 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. |
| Type | Conference paper [Without referee] |
| Conference | Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings |
| Editors | Shai Ben-David, John Case, Akira Maruoka |
| Year | 2004 Month September Vol. 3244 pp. 494 - 504 |
| Publisher | Springer-Verlag Heidelberg |
| Series | Lecture Notes in Computer Science |
| ISBN / ISSN | 3-540-23356-3 |
| Electronic version(s) | [pdf] |
| Publication link | http://www.springerlink.com/app/home/contribution.asp?wasp=4h83eykqtlckpl98rlrl&referrer=parent&backto=issue,37,37;journal,80,1851;linkingpublicationresults,1:105633,1 |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |