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 https://github.com/SRI-CSL/dnre.

翻译：我们提出了一种新的用于无似然仿真推断（SBI）的摊销似然比估计器。该估计器训练简单，且只需通过神经估计器的一次前向传播即可估计似然比。我们的方法直接计算两组竞争参数之间的似然比，这与先前比较两个神经网络输出值的方法不同。我们将此模型称为直接神经比率估计器（DNRE）。在提出DNRE的同时，我们推导了相应的后验蒙特卡洛估计。我们对该新比率估计器进行了基准测试，并与文献中先前的比率估计器进行了比较，结果表明新估计器通常优于先前方法。作为进一步贡献，我们引入了一种针对似然比估计器的新导数估计器，使得能够比较无似然哈密顿蒙特卡洛（HMC）与随机游走梅特罗波利斯-黑斯廷斯（MH）算法。我们证明了HMC同样具有竞争力，这一结论此前未被证实。最后，我们通过使用神经比率估计器设计四轴飞行器，展示了SBI的一个新颖现实应用。代码见https://github.com/SRI-CSL/dnre。