{"@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": [{"accessURL": "https://doi.org/10.18434/M32090", "title": "DOI access to Optimal Bayesian Experimental Design"}, {"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 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", "format": "Python source code, documentation in jupyter notebook, markdown and rst formats", "mediaType": "text/plain", "title": "Optimal Bayesian Experimental Design v. 0.1.8"}], "identifier": "8E5FC500E0A4777CE0532457068151792090", "issued": "2020-04-13", "keyword": ["GitHub pages template", "experimental design", "Bayesian", "optbayesexpt", "python", "measurement"], "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": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "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"}