Efficient computation of Bayesian optimal discriminating designs

TL;DR

This paper introduces an efficient algorithm for computing Bayesian optimal discriminating designs for various regression models, especially those with complex distributional assumptions, improving speed and accuracy over existing methods.

Contribution

A novel algorithm for Bayesian optimal discriminating designs based on the Kullback-Leibler criterion, effective for multiple models and complex distributions.

Findings

The new algorithm computes designs faster than existing methods.

It handles multiple competing models efficiently.

It achieves high accuracy in design determination.

Abstract

An efficient algorithm for the determination of Bayesian optimal discriminating designs for competing regression models is developed, where the main focus is on models with general distributional assumptions beyond the "classical" case of normally distributed homoscedastic errors. For this purpose we consider a Bayesian version of the Kullback- Leibler (KL) optimality criterion introduced by L\'opez-Fidalgo et al. (2007). Discretizing the prior distribution leads to local KL-optimal discriminating design problems for a large number of competing models. All currently available methods either require a large computation time or fail to calculate the optimal discriminating design, because they can only deal efficiently with a few model comparisons. In this paper we develop a new algorithm for the determination of Bayesian optimal discriminating designs with respect to the Kullback-Leibler criterion. It is demonstrated that the new algorithm is able to calculate the optimal discriminating designs with reasonable accuracy and computational time in situations where all currently available procedures are either slow or fail.