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Optimal Bayesian Experimental Design Version 1.0.1

Metadata Updated: July 29, 2022

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 11, 2021
Metadata Updated Date July 29, 2022
Data Update Frequency irregular

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date March 11, 2021
Metadata Updated Date July 29, 2022
Publisher National Institute of Standards and Technology
Maintainer
Identifier ark:/88434/mds2-2230
Language en
Data Last Modified 2020-04-01 00:00:00
Category Physics:Magnetics
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 a9bac87b-f42b-401c-b256-5817e8a0b509
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL https://data.nist.gov/od/id/mds2-2230
License https://www.nist.gov/open/license
Program Code 006:045
Source Datajson Identifier True
Source Hash 71a72cc83e4d2526eb463ade6bf1855d7f24fd5a
Source Schema Version 1.1

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