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.

翻译：状态空间模型（SSM）是对动态系统进行贝叶斯处理的标准形式体系，在统计学、信号处理和机器学习中具有天然应用。尽管动态系统在理论与应用中均至关重要，但将其融入现代概率编程语言（PPL）已被证明存在困难，这使得前沿方法难以被实践者广泛采用，并给遵循“贝叶斯工作流”带来阻碍。我们提出了dynestyx，一个为SSM提供一流支持的概率编程库，涵盖状态与参数估计的前沿方法。通过单一统一接口，用户可为离散时间或连续时间动态系统指定任意先验，对混合效应数据进行推理，并在进行状态与参数估计时实现原则性的不确定性量化。