We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

翻译：我们建议对多参数推理采用直觉、机学方法,称为InferoStatic Networks (ISN) 方法,在概率密度可以取样但不能直接计算的情况下进行分数和概率比估计器的模型。ISN使用一个后端神经网络,该网络模拟一个叫做电弧功能的后端神经网络,称为电弧潜在值$\varphie。此外,我们引入了新的战略,分别称为“KESE”和“Kernel Lislihlehood Rightimation”(KLRE),以便从模拟数据中学习得分数和概率比率函数。我们用一些小例子来说明新技术,并比较文献中的现有方法。我们提到一些新的损失功能,将模拟的潜在信息最佳地纳入培训程序。