# Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

@article{Delaunoy2022TowardsRS, title={Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation}, author={Arnaud Delaunoy and Joeri Hermans and Franccois Rozet and Antoine Wehenkel and Gilles Louppe}, journal={ArXiv}, year={2022}, volume={abs/2208.13624} }

Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice, the estimated posteriors’ computational faithfulness is, however, rarely guaranteed. For example, Hermans et al. [1] show that current simulation-based inference algorithms can produce posteriors that are overconﬁdent, hence risking false inferences. In this work, we introduce Balanced Neural Ratio Estimation ( BNRE ), a variation of the NRE…

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