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 some
work I have done 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 non-parametric
statistical independence test that shows progress towards a
conditional independence test for domains with continuous variables, a
problem currently unsolved in its generality.
Note that this talk is in partial fulfillment of the speaking requirement.
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Charles Rosenberg Last modified: Mon Apr 15 22:41:27 EDT 2002