Learning pairwise Markov network structures using correlation neighborhoods

30 Oct 2019Juri KuronenJukka CoranderJohan Pensar

Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from data without invoking chordality assumptions in order to retain expressiveness of the model class has been given a considerable attention in the recent literature, where numerous constraint-based or score-based methods have been introduced... (read more)

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