Generalized Bayesian inference in a fuzzy context: From theory to a virtual reality application

A generalized Bayesian inference framework in order to embed fuzzy sets and partial probabilistic information is provided. The general framework of reference is that of coherent conditional probabilities, which allows giving a rigorous interpretation of membership function as a conditional probability, regarded as a function of the conditioning event. The inferential problem needs to be studied in situations where the prior can be partial; moreover, membership and prior can be given on different classes of events. This inferential model is applied for the virtual representation of a female avatar.

► We provide a model in order to embed fuzzy sets and partial probabilistic information.
► The inferential problem faces situations where the prior can be partial.
► We study cases where membership and prior can be given on different classes of events.
► This inferential model is applied to the virtual representation of a female avatar.