Truncated Marginal Neural Ratio Estimation

• URL: http://arxiv.org/abs/2107.01214v1
• Date: Fri, 2 Jul 2021 18:00:03 GMT
• Title: Truncated Marginal Neural Ratio Estimation
• Authors: Benjamin Kurt Miller, Alex Cole, Patrick Forr\'e, Gilles Louppe, Christoph Weniger
• Abstract summary: We present a neural simulator-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior. By estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results.
• Score: 5.438798591410838
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural simulator-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability, which is unique among modern algorithms. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior and by proposing simulations targeted to an observation of interest via a prior suitably truncated by an indicator function. Furthermore, by estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results. Such tests are important for sanity-checking inference in real-world applications, which do not feature a known ground truth. We perform experiments on a marginalized version of the simulation-based inference benchmark and two complex and narrow posteriors, highlighting the simulator efficiency of our algorithm as well as the quality of the estimated marginal posteriors. Implementation on GitHub.

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