Optimal Bayesian Experimental Design v. 1.0.1
URL: https://github.com/usnistgov/optbayesexpt
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.
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Additional Information
| Field | Value |
|---|---|
| Data last updated | March 11, 2021 |
| Metadata last updated | September 24, 2025 |
| Created | March 11, 2021 |
| Format | Python source code, documentation in Jupyter notebook, markdown and rst formats |
| License | other-license-specified |
| Datastore active | False |
| Has views | False |
| Id | ab6d5a42-fe53-4e08-8044-c798c514fdf6 |
| Mimetype | text/plain |
| Package id | 14ff289b-3e71-4a59-8cef-88147d55befe |
| Position | 2 |
| State | active |