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Commutative Monads for Probabilistic Programming Languages

Authors: Xiaodong Jia 0002; Bert Lindenhovius; Michael W. Mislove; Vladimir Zamdzhiev;

Commutative Monads for Probabilistic Programming Languages

Abstract

A long-standing open problem in the semantics of programming languages supporting probabilistic choice is to find a commutative monad for probability on the category DCPO. In this paper we present three such monads and a general construction for finding even more. We show how to use these monads to provide a sound and adequate denotational semantics for the Probabilistic FixPoint Calculus (PFPC) -- a call-by-value simply-typed lambda calculus with mixed-variance recursive types, term recursion and probabilistic choice. We also show that in the special case where we consider continuous dcpo's, then all three monads coincide with the valuations monad of Jones and we fully characterise the induced Eilenberg-Moore categories by showing that they are all isomorphic to the category of continuous Kegelspitzen of Keimel and Plotkin.

Country
France
Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Calculus, [INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO], Computer Science - Programming Languages, Cost accounting, Mathematics - Category Theory, Computer science, Semantics, Logic in Computer Science (cs.LO), FOS: Mathematics, Category Theory (math.CT), Computer languages, Probabilistic logic, Programming Languages (cs.PL)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
Green