The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

859 theory lies at the crossroads between fuzzy sets, probability, and nonmonotonic
reasoning. Possibility theory is closely related to fuzzy sets if one considers that a possibility …

Fundamentals of Fuzzy Sets covers the basic elements of fuzzy set theory. Its four-part
organization provides easy referencing of recent as well as older results in the field. The first …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

This presents a range of theories about uncertainty, all of them mathematical and allowing
quantitative treatment. A definition of uncertainty is automatically associated with one of …

Three main semantics for membership functions seem to exist in the literature: similarity,
preference and uncertainty. Each semantics underlies a particular class of applications …

Possibility theory stands halfway between logical and probabilistic representation
frameworks. Possibility theory, as a setting for handling epistemic uncertainty, may have a …

Numerical possibility distributions can encode special convex families of probability
measures. The connection between possibility theory and probability theory is potentially …

This survey paper provides an overview of existing methods for building possibility
distributions. We both consider the case of qualitative possibility theory, where the scale …

In recent years there has been a major trend in uncertainty (more specifically, partial belief)
modelling emphasizing the idea that the degree of confidence in an event is not totally …