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Dealing with cycles in graph-based probabilistic models: the case of Logical Credal Networks

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Autor(es):
Cozman, Fabio G. ; Marinescu, Radu ; Lee, Junkyu ; Gray, Alexander ; Maua, Denis D.
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: NEURIPS WORKSHOPS, 2020; v. 290, p. 10-pg., 2025-01-01.
Resumo

We examine the consequences of directed cycles in graph-based representations of joint distributions, investigating the effect of cycles on Markov conditions and on Gibbs factorizations. We focus on Logical Credal Networks, a flexible and general formalism, showing that Koster's theory of Directed-Undirected Mixed Graphs (DUMGs) leads to an interesting Gibbs factorization. We show that inferences with DUMGs lead to multilinear programs. We also study the failure of global Markov conditions in cyclic structural equation models, connecting that failure to probabilistic imprecision under interventions. (AU)

Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
Beneficiário:Fabio Gagliardi Cozman
Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada
Processo FAPESP: 22/02937-9 - Indução de programas lógico-probabilístico-neurais
Beneficiário:Denis Deratani Mauá
Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial