Optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements

• Published in: Journal of research of the National Institute of Standards and Technology Vol. 126; pp. 126002 - 5
• Main Authors: McMichael, Robert D., Blakley, Sean M., Dushenko, Sergey
• Format: Journal Article
• Language: English
• Published: United States National Institute of Standards and Technology 2021; Superintendent of Documents; [Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology
• Subjects: Algorithms; Analysis; Decision theory; Design; Design of experiments; Efficiency; Experiments; Filters; Laptop computers; Measurement; Parameter estimation; Parameters; Probability distribution; Resampling; Simulation; Software; Standard deviation; particle flter; python; sequential Monte Carlo; automation; experiment design; Bayesian; parameter estimation; measurement
• ISSN: 2165-7254; 1044-677X; 2165-7254
• DOI: 10.6028/jres.126.002

Optbayesexpt is a public domain, 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 and online as measurements progress. 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. In a recent experiment, optbayesexpt yielded an order of magnitude increase in speed for measurement of a few narrow peaks in a broad spectral range.