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
Readings in model-based diagnosis
Readings in model-based diagnosis
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Combining knowledge from different sources in causal probabilistic models
The Journal of Machine Learning Research
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
A review of explanation methods for heuristic expert systems
The Knowledge Engineering Review
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Explanation of Bayesian Networks and Influence Diagrams in Elvira
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
A search problem in complex diagnostic Bayesian networks
Knowledge-Based Systems
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Debugging an expert system is virtually unfeasible without explanation facilities, especially in the case of probabilistic expert systems, whose way of reasoning is completely different from that of human experts. Unfortunately, almost currently available tools for building probabilistic graphical models offer no explanation facility. This paper shows how the explanation capabilities provided by Elvira, a software tool for editing and evaluating probabilistic graphical models, have helped us in the debugging of two medical Bayesian networks: Prostanet, for the diagnosis of prostate cancer, and hepar ii, for liver disorders.