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arXiv · 2201.12539

Data-Driven Parameter Estimation

George V. Moustakides
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Abstract

Optimum parameter estimation methods require knowledge of a parametric probability density that statistically describes the available observations. In this work we examine Bayesian and non-Bayesian parameter estimation problems under a data-driven formulation where the necessary parametric probability density is replaced by available data. We present various data-driven versions that either result in neural network approximations of the optimum estimators or in well defined optimization problems that can be solved numerically. In particular, for the data-driven equivalent of non-Bayesian estimation we end up with optimization problems similar to the ones encountered for the design of generative networks.

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George V. Moustakides. 2022-01-29. Data-Driven Parameter Estimation. https://arxiv.org/abs/2201.12539

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