Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions
Artificial Intelligence in Medicine
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Improving the therapeutic performance of a medical bayesian network using noisy threshold models
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Modeling causal reinforcement and undermining with Noisy-AND trees
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
A bayesian approach for on-line sum/count/max/min auditing on boolean data
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
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I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief-network representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications.