OptBayesExpt: Optimal Bayesian Experiment Design


R. D. McMichael




National Institute of Standards and Technology




December 22, 2022


This package offers an implementation of sequential Bayesian experiment design, an adaptive strategy for controlling experiments.

The optbayesexpt package is avaiable on github. https://www.github.com/usnistgov/optbayesexpt.

In publications using optbayesexpt, please cite

Robert D. McMichael, Sean M. Blakley, and Sergey Dushenko, Optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements, Journal of Research of National Institute of Standards and Technology, 126, 126002 (2021), https://doi.org/10.6028/jres.126.002.

What is it for?

It’s for making smart setting choices in measurements that determine the parameters of a model. It is for cases with

  • a known parametric model, i.e. an equation that relates unknown parameters and experimental settings to measurement predictions. Fitting functions used in least-squares fitting are good examples of parametric models.

  • an experiment (possibly computational) that uses a set-measure-repeat sequence with opportunities to change settings between measurements.

The benefit of these methods is that they provide settings choices that have the best chance of making the parameter estimates more precise. This feature is very helpful in situations where the measurements are expensive.

It is not primarily designed for analyzing existing data, but some of the code could be used with existing data to do Bayesian inference of parameter values.

Note that Bayesian optimization addresses a different problem: finding a maximum or minimum of an unknown function.

What does it do?

It chooses measurement settings “live” based on accumulated data.

The sequential Bayesian experimental design algorithms play the role of an impatient experimenter who monitors data from a running experiment and changes the measurement settings in order to get better, more meaningful data. Note the two steps here. The first step, looking at the data, is really an act of extracting meaning from the numbers, learning something about the system from the existing measurements. The second step, a decision-making step, is using that knowledge to improve the measurement strategy.

In the “looking at the data” role, the method uses Bayesian inference to extract and update information about model parameters as new measurement data arrives. Then, in the “decision making” role, the methods use the updated parameter knowledge to select settings that have the best chance of refining the parameters.

The most important role is the responsibility of the user. As delivered, the BayesOptExpt is ignorant of the world, and it’s the user’s responsibility to describe the world in terms of a reliable model, reasonable parameters, and reasonable experimental settings. As with most computer programs, “the garbage in, garbage out” rule applies.


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