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Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
François Rozet · Gilles Louppe

In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this context, performing Bayesian inference can be challenging. In this work, we present a novel method that enables amortized inference over arbitrary subsets of the parameters, without resorting to numerical integration, which makes interpretation of the posterior more convenient. Our method is efficient and can be implemented with arbitrary neural network architectures. We demonstrate the applicability of the method on parameter inference of binary black hole systems from gravitational waves observations.

Author Information

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.

Gilles Louppe (University of Liège)

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