Optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements

Optbayesexpt is a public domain, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function. Parameter estimation is the type of measurement one would conventionally tackle with a sequence of data acquisition steps followed by...

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Published inJournal of research of the National Institute of Standards and Technology Vol. 126; pp. 126002 - 5
Main Authors McMichael, Robert D., Blakley, Sean M., Dushenko, Sergey
Format Journal Article
LanguageEnglish
Published United States National Institute of Standards and Technology 2021
Superintendent of Documents
[Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology
Subjects
Algorithms
Analysis
Decision theory
Design
Design of experiments
Efficiency
Experiments
Filters
Laptop computers
Measurement
Parameter estimation
Parameters
Probability distribution
Resampling
Simulation
Software
Standard deviation
particle flter
python
sequential Monte Carlo
automation
experiment design
Bayesian
parameter estimation
measurement
Online AccessGet full text
ISSN2165-7254
1044-677X
2165-7254
DOI10.6028/jres.126.002

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Abstract Optbayesexpt is a public domain, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function. Parameter estimation is the type of measurement one would conventionally tackle with a sequence of data acquisition steps followed by fitting. The software is designed to provide data-based control of experiments, effectively learning from incoming measurement results and using that information to select future measurement settings live and online as measurements progress. The settings are chosen to have the best chances of improving the measurement results. With these methods optbayesexpt is designed to increase the efficiency of a sequence of measurements, yielding better results and/or lower cost. In a recent experiment, optbayesexpt yielded an order of magnitude increase in speed for measurement of a few narrow peaks in a broad spectral range.
AbstractList First introduced in 1993 as a bootstrap filter, [9] and also known as particle filters [10] or swarm filters, SMC methods are used in many diverse fields. Probability density can be directly adjusted through weights, and a resampling step ensures efficient computation, essentially by reassigning computational resources from low-weight particles to high-probability regions of parameter space. 3.Requirements The hardware requirements for optbayesexpt are met by many modern desktop and laptop computers capable of running Python 3.x. In a second phase, standard deviations drop rapidly as the algorithm focuses on a neighborhood of high-probability parameters and rules out other regions of parameter space. Acknowledgments S.D. and S.B. acknowledge support under the Cooperative Research Agreement between the University of Maryland and the National Institute of Standards and Technology Physical Measurement Laboratory, Award 70NANB14H209, through the University of Maryland.
Optbayesexpt is a public domain, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function. Parameter estimation is the type of measurement one would conventionally tackle with a sequence of data acquisition steps followed by fitting. The software is designed to provide data-based control of experiments, effectively learning from incoming measurement results and using that information to select future measurement settings live and online as measurements progress. The settings are chosen to have the best chances of improving the measurement results. With these methods optbayesexpt is designed to increase the efficiency of a sequence of measurements, yielding better results and/or lower cost. In a recent experiment, optbayesexpt yielded an order of magnitude increase in speed for measurement of a few narrow peaks in a broad spectral range.
ArticleNumber 126002
Audience Academic
Author Dushenko, Sergey
McMichael, Robert D.
Blakley, Sean M.
AuthorAffiliation 1 National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
2 Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD 20742 USA
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  surname: Blakley
  fullname: Blakley, Sean M.
  organization: National Institute of Standards and Technology, Physical Measurement Laboratory, Nanoscale Device Characterization Division, Gaithersburg, MD 20899, USA, University of Maryland, Institute for Research in Electronics and Applied Physics, , College Park, MD 20742, USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/39021357$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1063_5_0219929
crossref_primary_10_1063_5_0055630
crossref_primary_10_1103_PhysRevApplied_18_054001
crossref_primary_10_22331_q_2024_12_10_1555
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Cites_doi 10.1214/ss/1177009939
10.1016/j.jcp.2012.08.013
10.1109/MSP.2014.2330626
10.1214/aoms/1177728069
10.1103/PhysRevApplied.14.054036
10.1214/ss/1177013621
10.1098/rstl.1763.0053
10.1088/1367-2630/14/10/103013
10.1049/ip-f-2.1993.0015
10.1111/insr.12107
10.1049/ip-rsn:19990255
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Keywords particle flter
python
sequential Monte Carlo
automation
experiment design
Bayesian
parameter estimation
measurement
Language English
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Snippet Optbayesexpt is a public domain, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model...
First introduced in 1993 as a bootstrap filter, [9] and also known as particle filters [10] or swarm filters, SMC methods are used in many diverse fields....
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StartPage 126002
SubjectTerms Algorithms
Analysis
Decision theory
Design
Design of experiments
Efficiency
Experiments
Filters
Laptop computers
Measurement
Parameter estimation
Parameters
Probability distribution
Resampling
Simulation
Software
Standard deviation
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Title Optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements
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