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Message Passing Algorithms for Marginalization
Venkat Anantharam,
University of California, Berkeley:
Friday, March 14, 2003, 4:30 pm, Rutgers University, CoRE Auditorium:
The problem of finding marginals of a product function provides
a convenient unifying formulation for a wide range of problems in engineering
and science, including extremely important ones such as posterior
probability calculations in an estimation framework. Efficient
message passing algorithms are known for solving such problems when
certain conditional independencies are know to hold a priori.
The art of finding a good algorithm for such a problem is usually one of
finding a formulation of the problem with useful conditional independencies.
We will give a survey of this area, including a look at some of the
recently discoved connections with ideas from statistical physics.
We will then describe a
novel measure-theoretic view of the issues which subsumes significant parts
of the existing view.
This broadened view allows one to broaden the kinds of conditional
independencies one is looking for and can thus lead to efficient algorithms
of a rather unconventional kind,
relative to those one might discover with the existing formulations,
and which appear to have significantly lower complexity than the latter,
as we will try to demonstrate through examples.
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