MODDIS: a microcomputer program for model discrimination

A computer program for a microcomputer (HP 86) is presented to discriminate between different models and to design new experiments for model discrimination. By a non-linear fitting algorithm a set of experimental data is fitted with different models suggested by the user. The parameters characterizing each model are estimated by minimizing the sum of squared residuals; different criteria are used to test the choice between two or more models at different levels of probability and the smallest number of additional experiments required for discrimination is computed. If discrimination is not achieved a direct search method is used to find the local maxima of the divergence (or information for discrimination) over a user-chosen domain of independent variables (x R'''). The x value corresponding to the absolute maximum of the divergence is the best choice to run a new experiment for discrimination.