---
res:
  bibo_abstract:
  - We consider the problem of inference in agraphical model with binary variables.
    While in theory it is arguably preferable to compute marginal probabilities, in
    practice researchers often use MAP inference due to the availability of efficient
    discrete optimization algorithms. We bridge the gap between the two approaches
    by introducing the Discrete  Marginals technique in which approximate marginals
    are obtained by minimizing an objective function with unary and pair-wise terms
    over a discretized domain. This allows the use of techniques originally devel-oped
    for MAP-MRF inference and learning. We explore two ways to set up the objective
    function - by discretizing the Bethe free energy and by learning it  from training
    data. Experimental results show that for certain types of graphs a learned function
    can out-perform the  Bethe approximation. We also establish a link between the
    Bethe free energy and submodular functions.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Filip
      foaf_name: Korc, Filip
      foaf_surname: Korc
      foaf_workInfoHomepage: http://www.librecat.org/personId=476A2FD6-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Vladimir
      foaf_name: Kolmogorov, Vladimir
      foaf_surname: Kolmogorov
      foaf_workInfoHomepage: http://www.librecat.org/personId=3D50B0BA-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.15479/AT:IST-2012-0003
  dct_date: 2012^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2664-1690
  dct_language: eng
  dct_publisher: IST Austria@
  dct_title: Approximating marginals using discrete energy minimization@
...