Data-Driven Parameter Estimation
Applications
2022-02-01 v1 Signal Processing
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.
Cite
@article{arxiv.2201.12539,
title = {Data-Driven Parameter Estimation},
author = {George V. Moustakides},
journal= {arXiv preprint arXiv:2201.12539},
year = {2022}
}