Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2024-02-22
Metadata Last Updated:
2023-01-10 00:00:00
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| Title | Optimal Bayesian Experimental Design Version 1.2.0 |
|---|---|
| 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 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 | 2023-01-10 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | GitHub pages template , experimental design , Bayesian , optbayesexpt , python , adaptive measurement |
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