Dynestyx: A Probabilistic Programming Library for Dynamical Systems
It makes state-of-the-art Bayesian inference for dynamical systems more accessible to practitioners by integrating SSMs into a probabilistic programming framework.
Dynestyx introduces a probabilistic programming library with first-class support for state-space models, enabling users to specify priors for discrete- or continuous-time dynamical systems, perform inference over mixed-effect data, and obtain state and parameter estimates with uncertainty quantification.
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.