Using Prior Parameter Knowledge in Model-Based Design of Experiments for
Sequential model-based design of experiments (MBDoE) uses information
from previous experiments to select run conditions for new experiments.
Computation of the objective functions for popular MBDoE can be
impossible due to a non-invertible Fisher Information Matrix (FIM).
Previously, we evaluated a leave-out (LO) approach that design
experiments by removing problematic model parameters from the design
process. However, the LO approach can be computationally expensive due
to its iterative nature and some model parameters are ignored. In this
study, we propose a simple Bayesian approach that makes the FIM
invertible by accounting for prior parameter information. We compare the
proposed Bayesian approach to the LO approach for designing sequential
A-optimal experiments. Results from a pharmaceutical case study show
that the Bayesian approach is superior, on average, to the LO approach
for design of experiments. However, for subsequent parameter estimation,
a subset-selection-based LO approach gives better parameter values than
the Bayesian approach.