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                   B O S T O N   U N I V E R S I T Y
                      Computer Science Department
                          C O L L O Q U I U M
                      Wednesday, April 24, 11:00 AM
                       (Coffee served at 10:45 AM)
                         Seminar Room / MCS 135


                 Probabilistic Model Structure from Data


                         Dimitris Margaritis
                      Carnegie Mellon University


                               Abstract


Probabilistic models are useful for modeling non-deterministic data
generation processes.  Examples of these can be found in genetic
domains representing gene expression interactions, socio-economic
domains representing stock market prices influences by current events,
and many others.  The greatest problem in modeling such processes is
determining the structure of the model.  In my talk I will present
work I have done at Carnegie Mellon towards inferring the structure of
a specific class of models called Bayesian networks (BNs).  I will
present the GS ("grow-shrink") algorithm which uses conditional
independence tests to determine the BN structure.  I will also present
a statistical independence test that shows progress towards a
conditional independence test for domains with continuous variables, a
problem currently unsolved in its generality.  I will also show
some results of an application that uses Bayesian network models to
answer count queries from very large databases.  My approach features
constant time in the size of the database, a small space overhead, and
linear preprocessing time.  Moreover it is easily parallelizable and
can be readily used for data-mining purposes, at no extra cost.


Host: George Kollios
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