Structural perspective on constraint-based learning of Markov networks

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