Direct Amortized Likelihood Ratio Estimation
Adam D. Cobb Brian Matejek Daniel Elenius Anirban Roy Susmit Jha
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.