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Optimal Bayesian Experimental Design

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.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2019-07-22
Date Created: N/A
Views:
Data Provided by:
GitHub pages template
Dataset Owner: N/A

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Title 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 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.
Modified N/A
Publisher Name National Institute of Standards and Technology
Contact mailto:robert.mcmichael@nist.gov
Keywords GitHub pages template , experimental design , Bayesian , optbayesexpt , python , measurement
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            "title": "DOI access to Optimal Bayesian Experimental Design"
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            "title": "Documentation for Optimal Bayesian Experimental Design"
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            "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.",
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            "title": "Optimal Bayesian Experimental Design v. 0.1.8"
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