Simplified algorithms for adaptive experiment design in parameter estimation

TL;DR

This paper compares different utility algorithms for Bayesian adaptive experiment design in parameter estimation, showing that alternative utilities can significantly speed up computations with minimal loss in efficiency.

Contribution

It introduces and evaluates alternative utility algorithms for Bayesian experiment design, achieving faster computation while maintaining measurement effectiveness.

Findings

Alternative utilities significantly improve computational speed.

Minimal impact on measurement efficiency observed.

Simulated tests validate the effectiveness of proposed algorithms.

Abstract

In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, which is the predicted improvement of parameter distributions due to modeled measurement results. In this paper we compare information-theory-based utility with three alternative utility algorithms. Tests of these utility alternatives in simulated adaptive measurements demonstrate large improvements in computational speed with slight impacts on measurement efficiency.