Optimal Bayesian Experimental Design Version 1.0.1

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.

Data and Resources

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modified 2020-04-01 00:00:00
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Groups
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Tags
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  • AmeriGEOSS
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  • North America
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  • bayesian
  • experimental-design
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license_id other-license-specified
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maintainer Robert D. McMichael
maintainer_email robert.mcmichael@nist.gov
metadata_created 2025-09-24T00:01:38.661750
metadata_modified 2025-09-24T00:01:38.661758
notes 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.
num_resources 3
num_tags 14
title Optimal Bayesian Experimental Design Version 1.0.1