• Organization: Department of Commerce
• Updated: 2019-07-22 00:00:00

Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a parametric model -...

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• python source code, documentation in jupyter notebook, markdown and rst formats

• Organization: Department of Commerce
• Updated: 2020-04-01 00:00:00

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model -...

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• python source code, documentation in jupyter notebook, markdown and rst formats

• Organization: National Institute of Standards and Technology
• Updated: 2019-07-22 00:00:00

Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a parametric model -...

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• html
• python source code, documentation in jupyter notebook, markdown and rst formats

• Organization: National Institute of Standards and Technology
• Updated: 2023-01-10 00:00:00

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model -...

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• Organization: National Institute of Standards and Technology
• Updated: 2020-04-01 00:00:00

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model -...

• html
• html
• python source code, documentation in jupyter notebook, markdown and rst formats

• Organization: Department of Commerce
• Updated: 2023-01-10 00:00:00

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model -...

• plain text
• zip
• zip