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A considerable body of work in AI has been concerned with aggregating measures of confirmatory and disconfirmatory evidence for a common set of propositions. Claiming classical probability to be inadequate or inappropriate, several researchers have gone so far as to invent new formalisms and methods. We show how to represent two major such alternative approaches to evidential confirmation not only in terms of transformed (Bayesian) probability, but also in terms of each other. This unifies twoarXiv:1304.3439v1 fatcat:2md7qvuxknbkvaqvdsecgexqam