Optimal Bayesian Experimental Design Version 1.0.1

Published by National Institute of Standards and Technology | National Institute of Standards and Technology | Metadata Last Checked: June 27, 2025 | Last Modified: 2020-04-01 00:00:00

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in Python, and shared via GitHub's USNISTGOV organization.

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Complete Metadata

bureauCode	[ "006:55" ]
identifier	ark:/88434/mds2-2230
landingPage	https://data.nist.gov/od/id/mds2-2230
language	[ "en" ]
programCode	[ "006:045" ]
theme	[ "Physics:Magnetics" ]