Direct Amortized Likelihood Ratio Estimation


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.

In 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024
Adam Cobb
Adam Cobb
Senior Computer Scientist

My research interests include Bayesian inference, Bayesian deep learning, Gaussian processes, and quantum machine learning.

Susmit Jha
Susmit Jha
Technical Director, NuSCI

My research interests include artificial intelligence, formal methods, machine learning and dynamical systems.