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.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| NIST Pages Files | ZIP | Zip with files from NIST pages | Open resource |
| ORE Repo Files | ZIP | Zip file of ORE Repo | Open resource |
| README | Unknown | Special instructions for reviewers | Open resource |