Reactive Probabilistic Programming and Semi-Symbolic Inference

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• Guillaume Baudart (INRIA)
• Tuesday 20 August 2024, 12:00-12:30
• Seminar Room 1, Newton Institute.

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MIP - Modelling and inference for pandemic preparedness

Reactive synchronous languages are now a standard industry tool for critical embedded systems. Designers write high-level specifications by composing streams of values. Such systems typically evolve in noisy environments that can only be observed through noisy sensors. In this talk, I will present ProbZelus, a synchronous language extended with probabilistic constructs for Bayesian reasoning to model uncertainty. ProbZelus programs describe state-space models interacting with an observable environment. At runtime, an inference engine estimates the parameters of the model from observations to produce a stream of distributions. I will then detail the semi-symbolic inference algorithms that we use for efficient streaming inference which combine approximate sampling methods and exact symbolic computations.

This talk is part of the Isaac Newton Institute Seminar Series series.

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