We can, and should, do statistical inference on simulation models by adjusting the parameters in the simulation so that the values of {\em randomly chosen} functions of the simulation output match the values of those same functions calculated on the data. Results from the "state-space reconstruction" or "geometry from a time series'' literature in nonlinear dynamics indicate that just $2d+1$ such functions will typically suffice to identify a model with a $d$-dimensional parameter space. Results from the "random features" literature in machine learning suggest that using random functions of the data can be an efficient replacement for using optimal functions. In this preliminary, proof-of-concept note, I sketch some of the key results, and present numerical evidence about the new method's properties. A separate, forthcoming manuscript will elaborate on theoretical and numerical details.

翻译：我们可以也应该对模拟模型进行统计推断,调整模拟中的参数,使模拟输出的 {em 随机选择} 函数的值与根据数据计算的相同函数的值相匹配。 “ 状态- 空间重建” 或“ 时间序列文献中非线性动态的几何” 结果表明,这类函数通常只需2d+1美元即可确定一个具有美元- 维参数空间的模型。 机器学习中的“ 随机特征” 文献的结果表明,使用数据的随机功能可以有效地取代最佳功能。 在这个初步的概念证明说明中,我勾画了一些关键结果,并提出关于新方法属性的数字证据。 一份单独的、 即将出版的手稿将详细阐述理论和数字细节。