Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Context-specific sign-propagation in qualitative probabilistic networks
Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Neural Networks
Introducing the Discriminative Paraconsistent Machine (DPM)
Information Sciences: an International Journal
Biomedic Organizations: An intelligent dynamic architecture for KDD
Information Sciences: an International Journal
An investigation of critical factors in medical device development through Bayesian networks
Expert Systems with Applications: An International Journal
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We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in the case of inconsistent qualitative knowledge. A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features. Each inconsistent knowledge component uniquely defines a model class in the hyperspace. A set of constraints within each class is generated to describe the uncertainty in ground Bayesian model space. Quantitative Bayesian inference is approximated by model averaging with Monte Carlo methods. Our method is firstly benchmarked on ASIA network and is applied to a realistic biomolecular interaction modeling problem for breast cancer bone metastasis. Results suggest that our method enables consistently modeling and quantitative Bayesian inference by reconciling a set of inconsistent qualitative knowledge.