The Bayesian D-Optimal Design In Mixture Experimental Design

Abstract

Mixture design is known as experimental design which is often used. The total number of components in the mixture is 100% and the value of each component must be greater than or equal to 0%. The industry sector is usually used the mixture design. Then, the D-optimality criterion can help to determine the possible compositions of the mixture to conduct some trial and error composition of the product. However, this criterion very depend on the assumption of the model. To reduce its dependence, the Bayesian approximation is used. The Bayesian D-optimal algorithm applied to a mixture consisting of two components with constraint functions. Ten design points formed from eleven candidate points. By applying the Bayesian D-optimal algorithm on two components of the mixture, the design has no convergent design as the result. So, to find the result, the classical D-optimal was used and three different points was formed.