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A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
Joeri Hermans · Arnaud Delaunoy · François Rozet · Antoine Wehenkel · Volodimir Begy · Gilles Louppe

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in Machine Learning and the Physical Sciences »

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our results show that all benchmarked algorithms -- (S)NPE, (S)NRE, SNL and variants of ABC -- can yield overconfident posterior approximations, which makes them unreliable for scientific use cases and falsificationist inquiry. Failing to address this issue may reduce the range of applicability of simulation-based inference. For this reason, we argue that research efforts should be made towards theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective.In this regard, we show empirical evidence that ensembling posterior surrogates provides more reliable approximations and mitigates the issue.

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Author Information

Joeri Hermans (Unaffiliated)
Arnaud Delaunoy (Université de Liège)
François Rozet (University of Liège)

PhD student in deep learning applied to simulation-based inference and physics-informed learning under the supervision of Prof. Gilles Louppe at the University of Liège, Belgium.

Antoine Wehenkel (ULiège/Apple)
Volodimir Begy (University of Vienna)
Gilles Louppe (University of Liège)

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