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Simulation-Based Inference (SBI) is a promising Bayesian inference frameworkthat alleviates the need for analytic likelihoods to estimate posteriordistributions. Recent advances using neural density estimators in SBIalgorithms have demonstrated the ability to achieve high-fidelity posteriors,at the expense of a large number of simulations ; which makes their applicationpotentially very time-consuming when using complex physical simulations. Inthis work we focus on boosting the sample-efficiency of posterior densityestimation using the gradients of the simulator. We present a new method toperform Neural Posterior Estimation (NPE) with a differentiable simulator. Wedemonstrate how gradient information helps constrain the shape of the posteriorand improves sample-efficiency.

Neural Posterior Estimation with Differentiable Simulators