Observational Data and Optimal Experimental Design Discriminating between More than Two Models

In this work we focus on observational data and on the use of large data sets to implement an experimental design without additional runs, for an efficient use of these data, exploiting theory about optimal designs. More specifically, the proposed procedure is based on several steps and is an extension of a previous work. Furthermore, the final optimal design can be obtained discriminating between two models or more than two models. In this last case the suggested algorithm must be revised in order to discriminate between several equally close models assigning a weight to each one.The final goal is an experimental design that can be assumed as optimal by the point of view of a sequential optimality.