# Dynestyx: A Probabilistic Programming Library for Dynamical Systems

arXiv: https://arxiv.org/abs/2606.16985 · HTML: https://mlsanity.com/paper/2606.16985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Variational Sequential Monte Carlo](https://mlsanity.com/paper/1705.11140/) (2017)
- [Variational Gaussian Process State-Space Models](https://mlsanity.com/paper/1406.4905/) (2014)
- [Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series](https://mlsanity.com/paper/2301.11308/) (2023)
- [Data Assimilation: A Mathematical Introduction](https://mlsanity.com/paper/1506.07825/) (2015)
- [Stochastic Variational Inference for Hidden Markov Models](https://mlsanity.com/paper/1411.1670/) (2014)
- [Variational Bayes with Intractable Likelihood](https://mlsanity.com/paper/1503.08621/) (2015)
- [Stochastic Gradient MCMC Methods for Hidden Markov Models](https://mlsanity.com/paper/1706.04632/) (2017)
- [Particle Metropolis-Hastings using gradient and Hessian information](https://mlsanity.com/paper/1311.0686/) (2013)
- [Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter](https://mlsanity.com/paper/1906.02014/) (2019)
- [Particle Metropolis-adjusted Langevin algorithms](https://mlsanity.com/paper/1412.7299/) (2014)
- [Stochastic Gradient MCMC for Nonlinear State Space Models](https://mlsanity.com/paper/1901.10568/) (2019)
- [Stochastic Gradient MCMC for State Space Models](https://mlsanity.com/paper/1810.09098/) (2018)

## Abstract (arXiv)

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.