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.
Resources
3 resources available
-
DOI Access for Optimal Bayesian Experimental Design Version 1.0.1
FILE -
Documentation for Optimal Bayesian Experimental Design
FILE -
Optimal Bayesian Experimental Design v. 1.0.1
PYTH
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "Robert D. McMichael", "hasEmail": "mailto:robert.mcmichael@nist.gov" } |
| description | 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. |
| distribution |
[ { "title": "DOI Access for Optimal Bayesian Experimental Design Version 1.0.1", "accessURL": "https://doi.org/10.18434/M32230" }, { "title": "Documentation for Optimal Bayesian Experimental Design", "accessURL": "https://pages.nist.gov/optbayesexpt/" }, { "title": "Optimal Bayesian Experimental Design v. 1.0.1", "format": "Python source code, documentation in Jupyter notebook, markdown and rst formats", "mediaType": "text/plain", "description": "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.", "downloadURL": "https://github.com/usnistgov/optbayesexpt" } ] |
| identifier | ark:/88434/mds2-2230 |
| keyword |
[ "Bayesian", "GitHub pages template", "experimental design", "measurement", "optbayesexpt", "python" ] |
| landingPage | https://data.nist.gov/od/id/mds2-2230 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2020-04-01 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| theme |
[ "Physics:Magnetics" ] |
| title | Optimal Bayesian Experimental Design Version 1.0.1 |
