We present a method for approximating the semantics of probabilistic programs to the purpose of constructing semantics-based analyses of such programs. The method resembles the one based on Galois connection as developed in the Cousot framework for abstract interpretation. The main difference between our approach and the standard theory of abstract interpretation is the choice of linear space structures instead of order-theoretic ones as semantical (concrete and abstract) domains. We show that our method generates "best approximations" according to an appropriate notion of precision defined in terms of a norm. Moreover, if re-casted in a order-theoretic setting these approximations are correct in the sense of classical abstract interpretation theory. We use Concurrent ...
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