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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.
Resources
3 resources available
-
DOI access to Optimal Bayesian Experimental Design
FILE -
Documentation for Optimal Bayesian Experimental Design
FILE -
Optimal Bayesian Experimental Design v. 0.1.8
PYTHON SOURCE CODE, DOCUMENTATION IN JUPYTER NOTEBOOK, MARKDOWN AND RST FORMATS
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 |
