@CONFERENCE\{IMM2004-03459,
    author       = "M. Opper and O. Winther",
    title        = "Approximate Inference in Probabilistic Models",
    year         = "2004",
    month        = "sep",
    pages        = "494 - 504",
    booktitle    = "Algorithmic Learning Theory: 15th International Conference, {ALT} 2004, Padova, Italy, October 2-{5,} 2004. Proceedings",
    volume       = "3244",
    series       = "Lecture Notes in Computer Science",
    editor       = "Shai Ben-David, John Case, Akira Maruoka",
    publisher    = "Springer-Verlag Heidelberg",
    organization = "",
    address      = "",
    url          = "http://www.springerlink.com/app/home/contribution.asp?wasp=4h83eykqtlckpl98rlrl&referrer=parent&backto=issue,37,37;journal,80,1851;linkingpublicationresults,1:105633,1",
    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.",
    isbn_issn    = "3-540-23356-3"
}