标题： 骑术：常微分方程的参数推断的概率方法 显示英文标题

标题： rodeo: Probabilistic Methods of Parameter Inference for Ordinary Differential Equations

摘要： 常微分方程（ODEs）的参数估计在动态系统分析中起着基础性作用。 通常缺乏闭式解，ODEs 传统上使用确定性求解器进行近似。 然而，越来越多的证据表明，通过更好地考虑数值不确定性，概率 ODE 求解器可以产生更可靠的参数估计。 在这里，我们介绍了 rodeo，一个 Python 库，它提供了一个快速、轻量且可扩展的接口，用于广泛类别的概率 ODE 求解器，以及几种相关的参数推断方法。 其核心，rodeo 提供了一个概率求解器，其计算复杂度在评估点数量和系统变量数量上都是线性的。 此外，通过利用最先进的自动微分（AD）和即时（JIT）编译技术，rodeo 在多个示例中被证明能够为各种 ODE 系统提供快速、准确且可扩展的参数推断。 显示英文摘要

摘要： Parameter estimation for ordinary differential equations (ODEs) plays a fundamental role in the analysis of dynamical systems. Generally lacking closed-form solutions, ODEs are traditionally approximated using deterministic solvers. However, there is a growing body of evidence to suggest that probabilistic ODE solvers produce more reliable parameter estimates by better accounting for numerical uncertainty. Here we present rodeo, a Python library providing a fast, lightweight, and extensible interface to a broad class of probabilistic ODE solvers, along with several associated methods for parameter inference. At its core, rodeo provides a probabilistic solver that scales linearly in both the number of evaluation points and system variables. Furthermore, by leveraging state-of-the-art automatic differentiation (AD) and just-in-time (JIT) compiling techniques, rodeo is shown across several examples to provide fast, accurate, and scalable parameter inference for a variety of ODE systems.