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Scripts, data and plotting for "Simplified algorithms for adaptive experiment design in parameter estimation" v.2

Metadata Updated: July 29, 2022

Examples of adaptive measurement protocols using optimal Bayesian experiment design. This dataset supports "Simplified algorithms for adaptive experiment design in parameter estimation", arXiv 2202.08344 and submitted to Physical Review Applied. The calculations use python package optbayesexpt, which is available from The software applies to measurements of parameters in nonlinear parametric models. In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements.

Access & Use Information

Public: This dataset is intended for public access and use. License: See this page for license information.

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Metadata Created Date July 29, 2022
Metadata Updated Date July 29, 2022
Data Update Frequency irregular

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date July 29, 2022
Metadata Updated Date July 29, 2022
Publisher National Institute of Standards and Technology
Identifier ark:/88434/mds2-2585
Data First Published 2022-07-06
Language en
Data Last Modified 2022-03-08 00:00:00
Category Mathematics and Statistics:Experiment design, Mathematics and Statistics:Numerical methods and software
Public Access Level public
Data Update Frequency irregular
Bureau Code 006:55
Metadata Context
Schema Version
Catalog Describedby
Harvest Object Id 795165ef-fb2d-4887-a576-0f19260b6ae3
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL
Program Code 006:045
Related Documents,,
Source Datajson Identifier True
Source Hash 693cd76b8fc71b13a3bb1a69a25fe3b58632d4ff
Source Schema Version 1.1

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