Differentials and distances in probabilistic coherence spaces (extended version)

26 May 2020  ·  Ehrhard Thomas IRIF ·

In probabilistic coherence spaces, a denotational model of probabilistic functional languages, morphisms are analytic and therefore smooth. We explore two related applications of the corresponding derivatives... First we show how derivatives allow to compute the expectation of execution time in the weak head reduction of probabilistic PCF (pPCF). Next we apply a general notion of ''local'' differential of morphisms to the proof of a Lipschitz property of these morphisms allowing in turn to relate the observational distance on pPCF terms to a distance the model is naturally equipped with. This suggests that extending probabilistic programming languages with derivatives, in the spirit of the differential lambda-calculus, could be quite meaningful. read more

PDF Abstract
No code implementations yet. Submit your code now

Categories


Logic in Computer Science

Datasets


  Add Datasets introduced or used in this paper