In this paper we present an approach for reasoning about continuous context variables. We introduce a probabilistic mechanism based on efficient geometric structures that avoid typical restrictions of existing approaches. Especially, we can model non-Gaussian distributions, negated statements and we can deal with external knowledge that is only accessible on demand.