For some problems, generating observations (multivariate function values) is expensive, such as running a time-consuming computer code or conducting a real-world experiment. This can result in having only a few samples for high-dimensional input variables. These samples can be noisy and may not be available for some important ranges of the parameters. Examples include medical trials, large-scale computer simulations, such as climate models, and financial market data. This working group will study how to best use the available small samples and how strategically guide picking future samples. The group will consider algorithms based on assumptions about the underlying input-output relationship to improve the effectiveness of the statistical analysis under these difficult constraints. The assumption will be that the cost of the observations will far exceed the computational cost of the post-processing algorithms.
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SAMSI Directorate Liaison: Ilse Ipsen
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