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
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
Towards Effective Elicitation of NIN-AND Tree Causal Models
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
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 to be assessed for each node. It generally has the complexity exponential on n . The non-impeding noisy-AND (NIN-AND) tree is a recently developed causal model that reduces the complexity to linear, while modeling both reinforcing and undermining interactions among causes. Acquisition of an NIN-AND tree model involves elicitation of a linear number of probability parameters and a tree structure. Instead of asking the human expert to describe the structure from scratch, in this work, we develop a two-step menu selection technique that aids structure acquisition.