Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies
published: July 30, 2008, recorded: July 2008, views: 4788
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixed points of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to converge in many cases of interest. Moreover, the Bethe free energy is non-convex for graphical models with cycles thus introducing great difficulty in deriving efficient algorithms for finding local minima of the free energy for general graphs. In this paper we introduce two efficient BP-like algorithms, one sequential and the other parallel, that are guaranteed to converge to the global minimum, for any graph, over the class of energies known as ”convex free energies”. In addition, we propose an efficient heuristic for setting the parameters of the convex free energy based on the structure of the graph.
See Also:
Download slides:
uai08_hazan_cmpa.pdf (615.0 KB)
Download slides:
uai08_hazan_cmpa_01.ppt (6.0 MB)
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: