Optimal Bayesian Experimental Design v. 0.1.8
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 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.
Source: Optimal Bayesian Experimental Design
About this Resource
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| Name | Optimal Bayesian Experimental Design v. 0.1.8 |
| Format | Web Resource |
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| Created | 5 years ago |
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