Scripts, data and plotting for "Simplified algorithms for adaptive experiment design in parameter estimation" v.2
Examples of adaptive measurement protocols using optimal Bayesian experiment design. This dataset supports "Simplified algorithms for adaptive experiment design in parameter estimation", arXiv 2202.08344 and submitted to Physical Review Applied. The calculations use python package optbayesexpt, which is available from https://github.com/usnistgov/optbayesexpt. The software applies to measurements of parameters in nonlinear parametric models. In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements.
Resources
3 resources available
-
README
ENGLISH TEXT -
utility algorithms
FOLDERS CORRESPONDING TO FIGURES IN THE PAPER -
Simplified algorithms for adaptive experiment design in parameter estimation
PDF FORMATTED MANUSCRIPT
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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 | Examples of adaptive measurement protocols using optimal Bayesian experiment design. This dataset supports "Simplified algorithms for adaptive experiment design in parameter estimation", arXiv 2202.08344 and submitted to Physical Review Applied. The calculations use python package optbayesexpt, which is available from https://github.com/usnistgov/optbayesexpt. The software applies to measurements of parameters in nonlinear parametric models. In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements. |
| distribution |
[
{
"title": "README",
"format": "English text",
"mediaType": "text/plain",
"description": "A guide to the data in utility algorithms.zip",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2585/2585_README.txt"
},
{
"title": "utility algorithms",
"format": "Folders corresponding to figures in the paper",
"mediaType": "application/x-zip-compressed",
"description": "Data files and python code",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2585/utility%20algorithms.zip"
},
{
"title": "Simplified algorithms for adaptive experiment design in parameter estimation",
"format": "pdf formatted manuscript",
"mediaType": "application/pdf",
"description": "The associated paper",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2585/arXiv2202.08344.pdf"
}
]
|
| identifier | ark:/88434/mds2-2585 |
| issued | 2022-07-06 |
| keyword |
[
"Bayesian",
"Lorentzian",
"Ramsey",
"adaptive measurement",
"adaptive protocol",
"experiment design",
"experimental design",
"optimal design",
"parametric model",
"particle filter",
"sequential Monte Carlo",
"utility function"
]
|
| landingPage | https://data.nist.gov/od/id/mds2-2585 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2022-03-08 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| references |
[
"https://arxiv.org/abs/2202.08344",
"https://doi.org/10.6028/jres.126.002",
"https://pages.nist.gov/optbayesexpt"
]
|
| theme |
[
"Mathematics and Statistics:Experiment design",
"Mathematics and Statistics:Numerical methods and software"
]
|
| title | Scripts, data and plotting for "Simplified algorithms for adaptive experiment design in parameter estimation" v.2 |
