Description
Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a 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 |
|---|---|---|---|
| DOI access to Optimal Bayesian Experimental Design | Unknown | Open resource | |
| Optimal Bayesian Experimental Design v. 0.1.8 | Unknown | 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 efficeiently 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. | Open resource |
| Documentation for Optimal Bayesian Experimental Design | Unknown | Open resource |