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Reactive probabilistic programming

Abstract : Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or SCADE used routinely for implementing safety critical control software, e.g., fly-bywire and engine control in planes. However, to date these languages have had limited modern support for modeling uncertainty-probabilistic aspects of the software's environment or behavior-even though modeling uncertainty is a primary activity when designing a control system. In this paper we present ProbZelus the first synchronous probabilistic programming language. ProbZelus conservatively provides the facilities of a synchronous language to write control software, with probabilistic constructs to model uncertainties and perform inference-in-the-loop. We present the design and implementation of the language. We propose a measure-theoretic semantics of probabilistic stream functions and a simple type discipline to separate deterministic and probabilistic expressions. We demonstrate a semantics-preserving compilation into a first-order functional language that lends itself to a simple presentation of inference algorithms for streaming models. We also redesign the delayed sampling inference algorithm to provide efficient streaming inference. Together with an evaluation on several reactive applications, our results demonstrate that ProbZelus enables the design of reactive probabilistic applications and efficient, bounded memory inference.
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https://hal.inria.fr/hal-03051954
Contributor : Marc Pouzet <>
Submitted on : Thursday, December 10, 2020 - 1:49:22 PM
Last modification on : Saturday, December 12, 2020 - 3:53:56 AM

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Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, et al.. Reactive probabilistic programming. PLDI 2020 - 41th ACM SIGPLAN International Conference in Programming Language Design and Implementation, Jun 2020, London / Virtual, United Kingdom. ⟨10.1145/3385412.3386009⟩. ⟨hal-03051954⟩

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