We introduce a new amortized likelihood ratio estimator for likelihood-free
simulation-based inference (SBI). Our estimator is simple to train and
estimates the likelihood ratio using a single forward pass of the neural
estimator. Our approach directly computes the likelihood ratio between two
competing parameter sets which is different from the previous approach of
comparing two neural network output values. We refer to our model as the direct
neural ratio estimator (DNRE). As part of introducing the DNRE, we derive a
corresponding Monte Carlo estimate of the posterior. We benchmark our new ratio
estimator and compare to previous ratio estimators in the literature. We show
that our new ratio estimator often outperforms these previous approaches. As a
further contribution, we introduce a new derivative estimator for likelihood
ratio estimators that enables us to compare likelihood-free Hamiltonian Monte
Carlo (HMC) with random-walk Metropolis-Hastings (MH). We show that HMC is
equally competitive, which has not been previously shown. Finally, we include a
novel real-world application of SBI by using our neural ratio estimator to
design a quadcopter. Code is available at this https URL

我们引入了一种新的摊销似然比估计器，用于无似然模拟推理（SBI），引入了一种称为直接神经比率估计器（DNRE）的模型，对先前的方法进行了改进，并在文献中与其他估计器进行了比较，同时实现了一个新的导数估计器，使得我们可以比较无似然哈密顿蒙特卡洛（HMC）和随机行走 Metropolis-Hastings（MH），最后我们通过使用神经比率估计器设计四旋翼飞行器展示了该方法的现实应用。