Yanela Strappa

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Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables.(More)
Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes indepen-dences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures(More)
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