Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Higher order probability and intervals
International Journal of Approximate Reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Focusing vs. Belief Revision: A Fundamental Distinction When Dealing with Generic Knowledge
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
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
Probabilistic belief change: expansion, conditioning and constraining
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Indefinite Probabilities for General Intelligence
Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
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In the current discussion about the capacity of Bayesianism in reasoning under uncertainty, there is a conceptual and notational confusion between the explicit condition and the implicit condition of a probability evaluation. Consequently, the limitation of Bayesianism is often seriously underestimated. To represent the uncertainty of a belief system where revision is needed, it is not enough to assign a probability value to each belief.