Dynestyx: A Probabilistic Programming Library for Dynamical Systems
2026-06-15 • Machine Learning
Machine Learning
AI summaryⓘ
The authors introduce dynestyx, a new programming library designed to make it easier to work with state-space models (SSMs), which are tools used to understand systems that change over time. These models are important in fields like statistics and machine learning, but have been hard to use in current programming languages. Dynestyx provides a simple interface for users to set up these models, estimate hidden states and parameters, and handle uncertainties. This helps practitioners better use advanced Bayesian methods in their analyses.
state-space modelsBayesian inferencedynamical systemsprobabilistic programming languagesparameter estimationuncertainty quantificationdiscrete-time systemscontinuous-time systemsmixed-effect models
Authors
Daniel Waxman, Dmitry Batenkov, John Feser, Andy Zane, Eli Bingham, Youssef Marzouk, Matthew E. Levine
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.