Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on 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
Intercausal reasoning with uninstantiated ancestor nodes
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
Software project risk analysis using Bayesian networks with causality constraints
Decision Support Systems
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Independence of causal influence (ICI) offer a high level starting point for the design of Bayesian networks. However, these models are not as widely applied as they could, as their behavior is often not well-understood. One approach is to employ qualitative probabilistic network theory in order to derive a qualitative characterization of ICI models. In this paper we analyze the qualitative properties of ICI models with binary random variables. Qualitative properties are shown to follow from the characteristics of the Boolean function underlying the model. In addition, it is demonstrated that the theory also allows finding constraints on the model parameters given knowledge of the qualitative properties. This high-level qualitative characterization offers a new way of identifying suitable ICI models and may facilitate their exploitation in developing real-world Bayesian networks.