# Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption

@article{Antonucci2021StructuralLO, title={Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption}, author={Alessandro Antonucci and Alessandro Facchini and Lilith Mattei}, journal={ArXiv}, year={2021}, volume={abs/2107.12130} }

Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical LEARNSPN scheme to learn the structure of these models, we propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Sum nodes are thus learned by recursively clustering batches in the initial data base, while the partitioning of the variables obeys a given…

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