2023/06/13 by Ali Al Kadhim, H. Prosper, Kadhim, Ali Al +3 · 1 citation
Computer Science · Mathematics · #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Machine Learning and Data Classification #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2306.07769
openalex publication_date 2023/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.