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
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A new look at causal independence
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
Causal independence for knowledge acquisition and inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
A generalization of the noisy-or model
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
A Methodological Approach for the Effective Modeling of Bayesian Networks
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Toward Expert Knowledge Representation for Automatic Breast Cancer Detection
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
The Probabilistic Interpretation of Model-Based Diagnosis
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
EM algorithm for symmetric causal independence models
ECML'06 Proceedings of the 17th European conference on Machine Learning
A qualitative characterisation of causal independence models using boolean polynomials
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Improving the therapeutic performance of a medical bayesian network using noisy threshold models
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Qualitative chain graphs and their application
International Journal of Approximate Reasoning
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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.