A probabilistic approach for context reasoning
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.
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