Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtaining repeated numerical solutions of the differential equation(s). Recent results have explored how numerical truncation error can have major, detrimental, and sometimes hidden impacts on likelihood-based inference by introducing false local maxima into the log-likelihood function. We present a straightforward approach for inference that eliminates the need for solving the underlying differential equations, thereby completely avoiding the impact of truncation error. Open-access Jupyter notebooks, available on GitHub, allow others to implement this method for a broad class of widely-used models to interpret biological data.

翻译：在利用数学模型解释观测数据时，参数推断至关重要。针对微分方程模型的标准推断方法通常依赖于对微分方程进行重复数值求解。近期研究结果表明，数值截断误差可能在对数似然函数中引入虚假局部极大值，从而对基于似然的推断产生重大、有害且有时隐蔽的影响。我们提出一种无需求解底层微分方程的简易推断方法，从而完全避免了截断误差的影响。通过GitHub上提供的开源Jupyter笔记本，其他研究者可将此方法应用于广泛使用的模型类别，以解释生物学数据。