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Sean Blakley (Assoc)

Publications

Simplified algorithms for adaptive experiment design in parameter estimation

November 1, 2022

Author(s)

Robert D. McMichael, Sean Blakley

In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, i.e. the

Sequential Bayesian experiment design for adaptive Ramsey sequence measurements.

October 11, 2021

Author(s)

Robert D. McMichael, Sergey Dushenko, Sean Blakley

The Ramsey sequence is a canonical example of a quantum phase determination for a spin qubit, but when readout fidelity is low, as with NV centers, measurement

optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements

February 3, 2021

Author(s)

Robert McMichael, Sean M. Blakley, Sergey Dushenko

Optbayesexpt is a free, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function

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Created June 12, 2020, Updated February 21, 2023