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Summary: The Imprecise Noisy-OR Gate
Alessandro Antonucci
IDSIA
Lugano (Switzerland)
Email: alessandro@idsia.ch
Abstract--The noisy-OR gate is an important tool for a
compact elicitation of the conditional probabilities of a Bayesian
network. An imprecise-probabilistic version of this model, where
sets instead of single distributions are used to model uncertainty
about the inhibition of the causal factors, is proposed. This
transforms the original Bayesian network into a so-called credal
network. Despite the higher computational complexity generally
characterizing inference on credal networks, it is possible to prove
that, exactly as for Bayesian networks, the local complexity to
update probabilities on an imprecise noisy-OR gate takes only
linear, instead of exponential, time in the number of causes. This
result is also extended to fault tree analysis and allows for a
fast fusion of the causal effects on models with an imprecise-
probabilistic quantification of the initiating events.
Keywords: Noisy-OR gates, Bayesian networks, credal net-
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