Reactive probabilistic programming
Author(s)Atkinson, Eric Hamilton; Sherman, Benjamin; Carbin, Michael James
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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-by-wire 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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)
Baudart, Guillaume et al. “Reactive probabilistic programming.” Paper in the Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2020, PLDI ’20, London UK, June 15–20, 2020, ACM: 898-912 © 2020 The Author(s)
Author's final manuscript