Artificial Intelligence
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A methodology for uncertainty in knowledge-based systems
A methodology for uncertainty in knowledge-based systems
Second order probabilities for uncertain and conflicting evidence
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Updating with belief functions, ordinal conditional functions and possibility measures
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Experience-grounded semantics: a theory for intelligent systems
Cognitive Systems Research
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In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments are properly distinguished, it follows that Bayes' theorem is not a generally applicable revision rule. Upon properly distinguishing belief revision from belief updating, we see that Jeffrey's rule and its variations are not revision rules, either. Without these distinctions, the limitation of the Bayesian approach is often ignored or underestimated. Revision, in its general form, cannot be done in the Bayesian approach, because a probability distribution function alone does not contain the information needed by the operation.