Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods. (arXiv:2306.07769v2 [stat.ME] UPDATED)

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 …

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