MaxEnt Principle for Handling Uncertainty with Qualitative Values
Michèle Pappalardo · AIP conference proceedings · 2006
Bayesian mathematical model is the oldest method for modelling subjective degree of belief. If we have probabilistic measures with unknown values, then we must choose a different and appropriate model. The belief functions are a bridge between various models handling different forms of uncertainty. The conjunctive rule of Bayes builds a new set of a posteriori probability when two independent and accepted sets of random variable make inference. When two pieces of evidence are accepted with unknown values, the Dempster‐Shafer’s rule suggests a model for fusion of different degree of belief. In this paper we want to submit the use of MaxEnt principle for modelling the belief. Dealing with non‐Bayesian sets, in which the piece of evidence represents the belief instead of the knowledge, the MaxEnt principle gives a tool to reduce the number of subsets representing the frame of discernment. The fusion a focal set with a set of max entropy cause a Bayesian approximation reducing mass function to a probabilistic distribution.