Probabilistic Model Structure from Data

Dimitris Margaritis

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 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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