Design and analysis for the Gaussian Process model
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In an effort to speed the development of new products and processes, many companies are turning to computer simulations to avoid the time and expense of building prototypes. These computer simulations are often complex, taking hours to complete one run. If there are many variables affecting the results of the simulation, then it makes sense to design an experiment to gain the most information possible from a limited number of computer simulation runs. The researcher can use the results of these runs to build a surrogate model of the computer simulation model. The absence of noise is the key difference between computer simulation experiments and experiments in the real world. Since there is no variability in the results of computer experiments, optimal designs, which are based on reducing the variance of some statistic, have questionable utility. Replication, usually a "good thing", is clearly undesirable in computer experiments. Thus, a new approach to experimentation is necessary.
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