Approximate Inference in Probabilistic Models

Manfred Opper, Ole Winther

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearch


    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.
    Original languageEnglish
    Title of host publicationProceedings of the Conference on Algorithmic Learning Theory
    Publication date2004
    ISBN (Print)3-540-23356-3
    Publication statusPublished - 2004
    EventAnnual International Conference on Algorithmic Learning Theory - Padova, Italy
    Duration: 2 Oct 20045 Oct 2004
    Conference number: 15


    ConferenceAnnual International Conference on Algorithmic Learning Theory
    SeriesLecture Notes in Computer Science

    Cite this

    Opper, M., & Winther, O. (2004). Approximate Inference in Probabilistic Models. In Proceedings of the Conference on Algorithmic Learning Theory (Vol. 3244, pp. 494-504). Springer. Lecture Notes in Computer Science,37,37;journal,80,1851;linkingpublicationresults,1:105633,1