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.
Source: Optimal Bayesian Experimental Design Version 1.0.1
About this Resource
| Last updated | unknown |
|---|---|
| Created | unknown |
| Name | Optimal Bayesian Experimental Design v. 1.0.1 |
| Format | Web Resource |
| License | other-license-specified |
| Created | 5 years ago |
| Media type | text/plain |
| has views | False |
| id | ab6d5a42-fe53-4e08-8044-c798c514fdf6 |
| metadata modified | 5 years ago |
| package id | 14ff289b-3e71-4a59-8cef-88147d55befe |
| position | 2 |
| state | active |
| tracking summary | {'total': 0, 'recent': 0} |