Credal sum-product networks

Denis Deratani Mauá, Fabio Gagliardi Cozman, Diarmaid Conaty, Cassio Polpo de Campos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic graphical models that allow for marginal inference with polynomial effort. As with other probabilistic models, sum-product networks are often learned from data and used to perform classification. Hence, their results are prone to be unreliable and overconfident. In this work, we develop credal sum-product networks, an imprecise extension of sum-product networks. We present algorithms and complexity results for common inference tasks. We apply our algorithms on realistic classification task using images of digits and show that credal sum-product networks obtained by a perturbation of the parameters of learned sum-product networks are able to distinguish between reliable and unreliable classifications with high accuracy.

Original languageEnglish
Title of host publicationISIPTA'17: Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications
Number of pages12
Publication statusPublished - 2017
Externally publishedYes
Event10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 - Lugano, Switzerland
Duration: 10 Jul 201714 Jul 2017

Publication series

NameProceedings of Machine Learning Research


Conference10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017


  • Credal classification
  • Sum-product networks
  • Tractable probabilistic models


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