This paper is focused on adapting symmetry reduction, a technique that is highly successful in traditional model checking, to stochastic hybrid systems. To that end, we first sho...
Abstract We present a new approximate verification technique for falsifying the invariants of B models. The technique employs symmetry of B models induced by the use of deferred se...
We propose a novel a framework for deriving approximations for intractable probabilistic models. This framework is based on a free energy (negative log marginal likelihood) and ca...
In this paper we introduce “clipping,” a new method of syntactic approximation which is motivated by and works in conjunction with a sound and decidable denotational model for...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...