Frequentist parameter estimation with supervised learning

نویسندگان

چکیده

Recently, there has been a great deal of interest surrounding the calibration quantum sensors using machine learning techniques. This work explores use regression to infer machine-learned point estimate an unknown parameter. Although analysis is necessarily frequentist—relying on repeated estimates build up statistics—the authors clarify that this estimator converges Bayesian maximum posteriori (subject some regularity conditions). When number training measurements large, identical well-known maximum-likelihood (MLE), and fact, argue Cramér–Rao sensitivity bound applies mean-square error cost function can therefore be used select optimal model parameters. The inherits desirable asymptotic properties MLE, limit imposed by resolution grid. Furthermore, investigate role noise in process show imposes fundamental grid points. manuscript paves way for machine-learning assist sensors, thereby allowing inference play more prominent design operation next generation ultra-precise sensors.

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ژورنال

عنوان ژورنال: AVS quantum science

سال: 2021

ISSN: ['2639-0213']

DOI: https://doi.org/10.1116/5.0058163