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arXiv · 2306.07769

Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods

Ali Al Kadhim·Harrison B. Prosper·Olivia F. Prosper
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Abstract

High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. In this work, we introduce a simple extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. Like LF2I, this extension yields provably valid confidence sets in parameter inference problems in which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.

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BibTeXRIS

Ali Al Kadhim, Harrison B. Prosper, Olivia F. Prosper. 2023-06-13. Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods. https://arxiv.org/abs/2306.07769

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