{
    "identifier": "ark:\/88434\/mds2-2585",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Robert D. McMichael"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2585",
    "title": "Scripts, data and plotting for \"Simplified algorithms for adaptive experiment design in parameter estimation\" v.2",
    "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.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2585\/2585_README.txt",
            "format": "English text",
            "description": "A guide to the data in utility algorithms.zip",
            "mediaType": "text\/plain",
            "title": "README"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2585\/utility%20algorithms.zip",
            "format": "Folders corresponding to figures in the paper",
            "description": "Data files and python code",
            "mediaType": "application\/x-zip-compressed",
            "title": "utility algorithms"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2585\/arXiv2202.08344.pdf",
            "format": "pdf formatted manuscript",
            "description": "The associated paper",
            "mediaType": "application\/pdf",
            "title": "Simplified algorithms for adaptive experiment design in parameter estimation"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2022-03-08 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Mathematics and Statistics:Experiment design",
        "Mathematics and Statistics:Numerical methods and software"
    ],
    "keyword": [
        "Bayesian",
        "experiment design",
        "experimental design",
        "optimal design",
        "adaptive protocol",
        "adaptive measurement",
        "parametric model",
        "Ramsey",
        "Lorentzian",
        "particle filter",
        "sequential Monte Carlo",
        "utility function"
    ]
}