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