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Context Equivalences and Metrics in Probabilistic Lambda-Calculi

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If you have a question about this talk, please contact Dominic Mulligan .

Probabilistic models are more and more pervasive in computer science, and randomized algorithms are the ones offering the best performances in many domains. Higher-order probabilistic computation – in which probabilistic functions may be passed as a parameters and returned as results – is on the other hand a relatively underdeveloped field, which is however receiving more and more attention. We give a survey of what is known about the probabilistic lambda-calculi, later focusing on some of our recent results on coinductive techniques for proving program equivalent and for evaluating their distance. Finally, we hint at how all this could be useful when structuring proofs of security for cryptographic primitives.

This talk is part of the Logic and Semantics Seminar (Computer Laboratory) series.

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