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.
Optbayesexpt: Sequential Bayesian Experiment Design for Adaptive
Measurements
