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Explore a 25-minute conference presentation from POPL 2018 that delves into the development of a denotational model for probabilistic functional programming. Learn how researchers from the University of Paris Diderot and CNRS define stable and measurable maps between cones with measurability tests, demonstrating how these form a cpo-enriched cartesian closed category. Discover how this mathematical framework provides a foundation for modeling PCF extensions that incorporate key probabilistic programming features including continuous and discrete distributions, sampling, conditioning, and recursion. Understand the proven soundness and adequacy of this model in relation to call-by-name operational semantics, with practical examples of its applications in lambda calculus, program semantics, and probabilistic computation.
