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Optimal Bayesian Experimental Design
Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a 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 a 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 to Optimal Bayesian Experimental Design",
"accessURL": "https://doi.org/10.18434/M32090"
},
{
"title": "Documentation for Optimal Bayesian Experimental Design",
"accessURL": "https://pages.nist.gov/optbayesexpt/"
},
{
"title": "Optimal Bayesian Experimental Design v. 0.1.8",
"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 efficeiently 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 | 8E5FC500E0A4777CE0532457068151792090 |
| issued | 2020-04-13 |
| keyword |
[
"Bayesian",
"GitHub pages template",
"experimental design",
"measurement",
"optbayesexpt",
"python"
]
|
| landingPage | https://data.nist.gov/od/id/8E5FC500E0A4777CE0532457068151792090 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2019-07-22 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| references |
[
"https://doi.org/10.18434/M32090"
]
|
| theme |
[
"Mathematics and Statistics:Experiment design",
"Mathematics and Statistics:Numerical methods and software",
"Physics:Magnetics"
]
|
| title | Optimal Bayesian Experimental Design |
