On the applicability of maximum entropy to inexact reasoning
International Journal of Approximate Reasoning
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Asymptomatic conditional probabilities for first-order logic
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A method for updating that justifies minimum cross entropy
International Journal of Approximate Reasoning
Statistical foundations for default reasoning
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Default reasoning from statistics
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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A logic is defined that allows to express information about statistical probabilities and about degrees of belief in specific propositions. By interpreting the two types of probabilities in one common probability space, the semantics given are well suited to model the influence of statistical information on the formation of subjective beliefs. Cross entropy minimization is a key element in these semantics, the use of which is justified by showing that the resulting logic exhibits some very reasonable properties.