Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods
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.
Cite
@article{arxiv.2306.07769,
title = {Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods},
author = {Ali Al Kadhim and Harrison B. Prosper and Olivia F. Prosper},
journal= {arXiv preprint arXiv:2306.07769},
year = {2023}
}