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