Optimal Bayesian Experimental Design Version 1.0.1

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.

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Documentation for Optimal Bayesian Experimental Design
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Optimal Bayesian Experimental Design v. 1.0.1Python source code, documentation in Jupyter notebook, markdown and rst formats
Python module 'optbayesexpt' uses optimal Bayesian experimental design...
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Pole	Hodnota
Správca	Robert D. McMichael
Posledná aktualizácia	Marec 5, 2021, 07:22 (EST)
Vytvorené	Marec 5, 2021, 07:22 (EST)
Identifier	ark:/88434/mds2-2230
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Modified	2020-04-01 00:00:00
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