Learning Chordal Markov Networks by Constraint Satisfaction

NeurIPS 2013 Jukka CoranderTomi JanhunenJussi RintanenHenrik NymanJohan Pensar

We investigate the problem of learning the structure of a Markov network from data. It is shown that the structure of such networks can be described in terms of constraints which enables the use of existing solver technology with optimization capabilities to compute optimal networks starting from initial scores computed from the data... (read more)

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