<title>Classification when a priori evidence is ambiguous</title>

This paper describes conditioned Dempster-Shafer (CDS) theory, a probabilistic calculus for dealing with possibly non-Bayesian evidence when underlying a priori knowledge is possibly non-Bayesian. The Dempster-Shafer composition operator can be `conditioned' to reflect the influence of any kind of a priori knowledge which can be modeled as a Dempster-Shafer belief measure. CDS is firmly grounded in probability theory via the theory of random sets. It is also a generalization of the Bayesian theory of evidence to the case when both evidence and a priori knowledge are ambiguous.