Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Principles of expert systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
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
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
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The certainty-factor model was one of the most popular models for the representation and manipulation of uncertain knowledge in the early rule-based expert systems of the 1980s. After the model was criticised by researchers in artificial intelligence and statistics as being ad-hoc in nature, researchers and developers have stopped using the model. Nowadays, it is often stated that the model is merely interesting from a historical point of view. Its place has been taken by more expressive formalisms for the representation and manipulation of uncertain knowledge, in particular by the formalism of Bayesian networks. In this paper, it is shown that this view underestimates the importance of the principles underlying the certainty-factor model. In particular, it is shown that certainty-factor-like structures occur frequently in practical Bayesian network models as causal independence assumptions. In fact, the noisy-OR and noisy-AND models, two probabilistic models frequently employed, appear to be reinventions of combination functions previously introduced as part of the certainty-factor model. This insight may lead to a reappraisal of the certainty-factor model.