Transcripción del resumen del autor: The Maximization of Matching-Targets' Probability for Unevaluated Results (MMTPUR), technique presented in this paper, is based on the classical probabilistic optimization framework. The numerical function values that have not been evaluated are considered as stochastic functions. Thus, a Gaussian process uncertainty model is built for each required numerical function result (i.e., associated with each specified target) and is used to estimate probability density functions for unevaluated results. Parameter posterior distributions, used within the optimization process, then take into account these probabilities. This approach is particularly adapted when, getting one evaluation of the numerical function is very time consuming. In this paper, we provide a detailed outline of this technique. Finally, several test cases are developed to stress its potential.

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