A Generalization of the Noisy-Or Model
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Abstract (arXiv)
The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for construction of Bayesian networks. We illustrate with some examples including digital circuit diagnosis and network reliability analysis.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
5 papers.
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- 2025Surjective Independence of Causal Influences for Local Bayesian Network Structures29 Sep 2025
- 2024Causal knowledge engineering: A case study from COVID-1921 Mar 2024
- 2024α-HMM: A Graphical Model for RNA Folding7 Jan 2024
- 2022Giga-SSL: Self-Supervised Learning for Gigapixel Images6 Dec 2022
- 2021A Causal Approach for Unfair Edge Prioritization and Discrimination Removal29 Nov 2021