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A general theory of prior probability models is presented, valid for both discrete and continuous random variables, even when the prior information about them has been obtained with errors. An example is included as an illustration. 1doi:10.1016/s0019-9958(73)90448-8 fatcat:yay6syc5mvbhtgjohn2sm53qtq