Simplified Algorithms for Adaptive Experiment Design in Parameter Estimation

Measurements to estimate parameters of a model are commonplace in the physical sciences, where the traditional approach to automation is to use a sequence of preselected settings followed by least-squares fitting of a model function to the data. This measure-then-fit approach is simple and effective and entirely appropriate for many applications but when measurement resources are limited, efficiency becomes more important. To increase efficiency, Bayesian experiment design allows measurement settings to be chosen adaptively based on accumulated data and utility, the predicted improvement in results as a function of settings. However, the calculation of utility has been judged too impractical for most applications. In this paper, we introduce computational methods and simplified algorithms that accelerate utility calculations by over an order of magnitude, with only slight degradation in measurement efficiency. The methods eliminate utility calculation as a barrier to practical application of efficient adaptive measurement.

Automated summary

This paper introduces computational methods and simplified algorithms that accelerate utility calculations, eliminating the barrier of utility calculation for practical application of efficient adaptive measurement.