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

Various semantics for studying the square of opposition and the hexagon of opposition have
been proposed recently. We interpret sentences by imprecise (set-valued) probability …

There is wide support in logic, philosophy, and psychology for the hypothesis that the
probability of the indicative conditional of natural language, P (if A then B), is the conditional …

Some results on coherence in probabilistic and in possibilistic frameworks are presented in
order to deal with nonmonotonic reasoning. Moreover, we extend these results to …

Conditioning for (non-additive) uncertainty measures is still an open problem. This is
essentially due to the fact that these measures can be viewed either as lower or upper …

Data-driven extraction of knowledge naturally takes advantage from the use of statistics,
since statistical approaches enable to summarize information embedded in the dataset. On …

Likelihood functions are studied in a probabilistic and possibilistic setting: inferential
conclusions are drawn from a set of likelihood functions and prior information relying on the …

Any assessment formed by a strategy and a prior probability is a coherent conditional
probability and can be extended, generally not in a unique way, to a full conditional …

Uncertainty is at the heart of decision-making processes in most real-world applications.
Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory …

Starting from a likelihood function and a prior information represented by a belief function, a
closed form expression is provided for the lower envelope of the set of all the possible …