The complexity of Boolean functions
The complexity of Boolean functions
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh 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
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
A generic qualitative characterization of independence of causal influence
International Journal of Approximate Reasoning
Learning symmetric causal independence models
Machine Learning
Enhanced qualitative probabilistic networks for resolving trade-offs
Artificial Intelligence
Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions
Artificial Intelligence in Medicine
Self-organizing content management with semantic neural networks
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
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In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to offer developers a high-level starting point when developing Bayesian networks.