Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation
IEEE Transactions on Knowledge and Data Engineering
Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
The enumeration of trees by height and diameter
IBM Journal of Research and Development
Recursive noisy OR - a rule for estimating complex probabilistic interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Indirect elicitation of NIN-AND trees in causal model acquisition
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
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To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. It generally has the complexity exponential on n . Noisy-OR reduces the complexity to linear, but can only represent reinforcing causal interactions. The non-impeding noisy-AND (NIN-AND) tree is the first causal model that explicitly expresses reinforcement, undermining, and their mixture. It has linear complexity, but requires elicitation of a tree topology for types of causal interactions. We study their topology space and develop two novel techniques for more effective elicitation.