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Dynestyx addresses a critical gap in probabilistic programming by offering native support for state-space models (SSMs), which are foundational for Bayesian analysis of dynamical systems but historically difficult to implement. This library simplifies the workflow for estimating states and parameters across discrete or continuous-time domains, enabling more robust uncertainty quantification directly within modern inference frameworks.
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
arXiv:2606.16985v1 Announce Type: cross Abstract: State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in following the "Bayesian workflow." We introduce dynestyx, a probabilistic programming library with first-class support for SSMs, including state-of-the-art methods in the estimation of both states and parameters. Through a single, unified interface, users may specify arbitrary priors for discrete-time or continuous-time dynamical systems, perform inference over mixed-effect data, and make state and parameter estimates with principled uncertainty quantification.
Abstract:State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in following the "Bayesian workflow." We introduce dynestyx, a probabilistic programming library with first-class support for SSMs, including state-of-the-art methods in the estimation of both states and parameters. Through a single, unified interface, users may specify arbitrary priors for discrete-time or continuous-time dynamical systems, perform inference over mixed-effect data, and make state and parameter estimates with principled uncertainty quantification.
Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP); Chaotic Dynamics (nlin.CD); Methodology (stat.ME)
arXiv:2606.16985 [stat.ML]
(or arXiv:2606.16985v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2606.16985 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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