让动态系统推理更简单，支持状态与参数的精确不确定性估计。
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
- 用统一接口定义离散/连续时间动态系统的先验
- 支持混合效应数据下的状态与参数联合推断
- 适合需要严谨不确定性建模的科研与工程人员
状态空间模型（SSMs）是贝叶斯处理动态系统的核心形式，广泛应用于统计学、信号处理和机器学习。尽管理论与应用重要，现代概率编程语言（PPLs）对动态系统的支持仍显不足，导致先进方法难以被实践者使用，并在遵循“贝叶斯工作流程”时产生摩擦。我们提出 dynestyx，一个原生支持 SSM 的概率编程库，包含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断，并实现带有合理不确定性量化的状态与参数估计。
原文摘要 · Abstract (English)
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.
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