论文标题
基于模拟的推理的新机器学习技术：推论网，内核得分估计和内核似然比估计
New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation
论文作者
论文摘要
我们提出了一种直观的，机器学习的方法，用于多参数推理，被称为推论网络（ISN）方法，以在可以采样但不能直接计算的概率密度的情况下对得分和似然比估计器进行建模。 ISN使用后端神经网络，该网络对标量函数进行建模，称为推论势$φ$。此外，我们分别介绍了新策略，分别称为内核得分估计（KSE）和内核似然比估计（KLRE），以从模拟数据中学习得分和似然比函数。我们用一些玩具示例说明了新技术，并与文献中的现有方法相比。我们提到了一些新的损失功能，可以将模拟中的潜在信息最佳地纳入培训程序。
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 $φ$. 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.
