Bayesian Decision Making with Previous Probabilistic Uncertainty and Actual Fuzzy Imprecision

The traditional literature dealing with statistical decision problems usually assumes that previous information about an associated experiment may be expressed by means of conditional probabilistic information, and the actual experimental outcomes can be perceived with exactness by the statistician. We now consider statistical decision problems satisfying the first assumption above, so that the actual available information cannot be exactly perceived, but rather it may be assimilated with fuzzy information (as defined by Zadeh et al.).