  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.
