Construction of predictive be-lief functions using a frequentist approach

This paper addresses the problem of building belief functions from statistical data. We describe a method for quantifying an agent belief about the realization of a discrete random variable X with unknown probability distribution PX , having observed a realization of an iid random sample with the same distribution. The proposed solution verifies two “reasonable” properties with respect to PX : it is less committed than PX with some user-defined probability, and it converges towards PX in probability as the size of the sample tends to infinity.

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