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
A generic qualitative characterization of independence of causal influence
International Journal of Approximate Reasoning
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
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
Knowledge engineering for large belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Parameter adjustment in Bayes networks. the generalized noisy OR-gate
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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
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
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To specify a Bayesian network (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, must be assessed for each node. Its complexity is generally exponential in n. Noisy-OR and a number of extensions reduce the complexity to linear, but can only represent reinforcing causal interactions. Non-impeding noisy-AND (NIN-AND) trees are the first causal models that explicitly express reinforcement, undermining, and their mixture. Their acquisition has a linear complexity, in terms of both the number of parameters and the size of the tree topology. As originally proposed, however, they allow only binary effects and causes. This work generalizes binary NIN-AND tree models to multi-valued effects and causes. It is shown that the generalized NIN-AND tree models express reinforcement, undermining, and their mixture at multiple levels, relative to each active value of the effect. The model acquisition is still efficient. For binary variables, they degenerate into binary NIN-AND tree models. Hence, this contribution enables CPTs of discrete BNs of arbitrary variables (binary or multi-valued) to be specified efficiently through the intuitive concepts of reinforcement and undermining.