A note on the inevitability of maximum entropy
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
A method for updating that justifies minimum cross entropy
International Journal of Approximate Reasoning
A representation theorem and applications to measure selection and noninformative priors
International Journal of Approximate Reasoning
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Degrees of belief are formed using observed evidence and statistical background information. In this paper we examine the process of how prior degrees of belief derived from the evidence are combined with statistical data to form more specific degrees of belief. A statistical model for this process then is shown to vindicate the cross-entropy minimization principle as a rule for probabilistic default-inference.