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.
Resources
| Name |
Format |
Description |
Link |
|
0 |
Special instructions for reviewers |
https://data.nist.gov/od/ds/mds2-2908/Review%20README.txt |
|
57 |
Zip file of ORE Repo |
https://data.nist.gov/od/ds/mds2-2908/obe-repo.zip |
|
57 |
Zip with files from NIST pages |
https://data.nist.gov/od/ds/mds2-2908/nist-pages.zip |
Tags
- bayesian
- experimental-design
- adaptive-measurement
- python
- github-pages-template
- optbayesexpt