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Structure learning in Bayesian Networks

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Probabilistic graphical models are commonly used in the machine learning community. They provide a simple way to design and visualize the structure of the probability model and allow some complex computations to be expressed in terms of graphical manipulations. Inference in those models has been widely studied and is included to some extent in the undergraduate courses (AI2). However, these assume that the structure of the graph is known a priori and remains unaltered. In this lecture I will focus on the most common methods used to infer the structure of Bayesian networks, that is, it underlying graph, directly from the data.

This talk is part of the Computer Laboratory Research Students' Lectures 2014 series.

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