Bayesian T-optimal discriminating designs

TL;DR

This paper introduces an efficient method for constructing Bayesian T-optimal discriminating designs in regression models, capable of handling over 100 competing models, surpassing existing procedures in performance.

Contribution

A novel algorithm combining exchange and gradient methods is developed, enabling the construction of Bayesian T-optimal discriminating designs for large model sets.

Findings

The new method converges reliably to optimal designs.

It outperforms existing procedures in handling many models.

It can manage over 100 competing models efficiently.

Abstract

The problem of constructing Bayesian optimal discriminating designs for a class of regression models with respect to the T-optimality criterion introduced by Atkinson and Fedorov (1975a) is considered. It is demonstrated that the discretization of the integral with respect to the prior distribution leads to locally T-optimal discrimination designs can only deal with a few comparisons, but the discretization of the Bayesian prior easily yields to discrimination design problems for more than 100 competing models. A new efficient method is developed to deal with problems of this type. It combines some features of the classical exchange type algorithm with the gradient methods. Convergence is proved and it is demonstrated that the new method can find Bayesian optimal discriminating designs in situations where all currently available procedures fail.