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    • Optimal Bayesian Experimental Design Version 1.2.0

    Metadata Updated: March 18, 2023

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: See this page for license information.

    Downloads & Resources

    Dates

    Metadata Created Date March 18, 2023
    Metadata Updated Date March 18, 2023
    Data Update Frequency irregular

    Metadata Source

    Harvested from NIST

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date March 18, 2023
    Metadata Updated Date March 18, 2023
    Publisher National Institute of Standards and Technology
    Maintainer
    Identifier ark:/88434/mds2-2908
    Data First Published 2023-02-22
    Language en
    Data Last Modified 2023-01-10 00:00:00
    Category Mathematics and Statistics:Experiment design
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 006:55
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 0309633e-3bb7-4943-807b-c1ac20d57567
    Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
    Harvest Source Title NIST
    Homepage URL https://pages.nist.gov/optbayesexpt/
    License https://www.nist.gov/open/license
    Program Code 006:045
    Source Datajson Identifier True
    Source Hash 2fd7cb15d3fdaaf3f1a0c30c87e4546d02f920e4
    Source Schema Version 1.1

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