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PUBLICATIONS

optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements

Published

Author(s)

Robert McMichael, Sean M. Blakley, Sergey Dushenko

Abstract

Optbayesexpt is a free, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function. Parameter estimation is the type of measurement one would conventionally tackle with a sequence of data acquisition steps followed by fitting. The software is designed to provide data-based control of experiments, effectively learning from incoming measurement results and using that information to select future measurement settings "live," "in real time," "online," "on the fly." The settings are chosen to have the best chances of improving the measurement results. With these methods optbayesexpt is designed to increase the efficiency of a sequence of measurements, yielding better results and/or lower cost.
Citation
Journal of Research of the National Institute of Standards and Technology
Volume
126
Pub Type
Journals

Download Paper

https://dx.doi.org/10.6028/jres.126.002
Local Download

Keywords

automation, Bayesian, experiment design, measurement, python, parameter estimation, sequential Monte Carlo, particle filter
Numerical methods and software and Experiment design

Citation

McMichael, R. , Blakley, S. and Dushenko, S. (2021), optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements, Journal of Research of the National Institute of Standards and Technology, [online], https://dx.doi.org/10.6028/jres.126.002 (Accessed October 25, 2025)

Issues

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Created February 2, 2021, Updated March 1, 2021
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