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Optimal Bayesian Experimental Design Version 1.2.0
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.
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": "README",
"format": "plain text",
"mediaType": "text/plain",
"description": "Special instructions for reviewers",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2908/Review%20README.txt"
},
{
"title": "ORE Repo Files",
"format": "ZIP",
"mediaType": "application/gzip",
"description": "Zip file of ORE Repo",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2908/obe-repo.zip"
},
{
"title": "NIST Pages Files",
"format": "ZIP",
"mediaType": "application/gzip",
"description": "Zip with files from NIST pages",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2908/nist-pages.zip"
}
]
|
| identifier | ark:/88434/mds2-2908 |
| issued | 2023-02-22 |
| keyword |
[
"Bayesian",
"GitHub pages template",
"adaptive measurement",
"experimental design",
"optbayesexpt",
"python"
]
|
| landingPage | https://pages.nist.gov/optbayesexpt/ |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2023-01-10 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| theme |
[
"Mathematics and Statistics:Experiment design"
]
|
| title | Optimal Bayesian Experimental Design Version 1.2.0 |
