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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.

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

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date March 18, 2023
Metadata Updated Date March 18, 2023
Publisher National Institute of Standards and Technology
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
Schema Version
Catalog Describedby
Harvest Object Id 0309633e-3bb7-4943-807b-c1ac20d57567
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL
Program Code 006:045
Source Datajson Identifier True
Source Hash 2fd7cb15d3fdaaf3f1a0c30c87e4546d02f920e4
Source Schema Version 1.1

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