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Simplified algorithms for adaptive experiment design in parameter estimation

Published

Author(s)

Robert D. McMichael, Sean Blakley

Abstract

In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, i.e. the predicted improvement of parameter distributions due to modeled measurement results. In this paper we compare information theory based utility with three alternative utility algorithms. Tests of these utility alternatives in simulated adaptive measurements demonstrate large improvements in computational speed with slight impacts on measurement effciency.
Citation
Physical Review Applied
Volume
18
Issue
5

Keywords

Bayesian experiment design, utility

Citation

McMichael, R. and Blakley, S. (2022), Simplified algorithms for adaptive experiment design in parameter estimation, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.18.054001, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934160 (Accessed January 28, 2023)
Created November 1, 2022, Updated November 29, 2022