  In this paper, we consider several types of information and methods of
combination associated with incomplete probabilistic systems. We discriminate
between 'a priori' and evidential information. The former one is a description
of the whole population, the latest is a restriction based on observations for
a particular case. Then, we propose different combination methods for each one
of them. We also consider conditioning as the heterogeneous combination of 'a
priori' and evidential information. The evidential information is represented
as a convex set of likelihood functions. These will have an associated
possibility distribution with behavior according to classical Possibility
Theory.
