March 14, 2024, 4:42 a.m. | Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen

cs.LG updates on

arXiv:2403.08562v1 Announce Type: new
Abstract: Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-based structure learning, which entails learning the undirected graph from data through the execution of conditional independence tests. We establish theoretical limits concerning two critical aspects of constraint-based learning of Markov networks: the number of tests and the sizes of the conditioning sets. These bounds uncover an exciting interplay between the structural properties of …

abstract arxiv cs.lg data focus graph graphs lies markov networks perspective relationships tests through type variables

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