Probabilistic Surrogate Model
A probabilistic surrogate model is a statistical model that is used to approximate the behavior of a complex system or process. It is often used in situations where the actual system is too expensive or time-consuming to simulate directly. The surrogate model is trained on a set of input-output pairs generated by the actual system, and it learns to predict the output for any given input. The model also provides a measure of uncertainty in its predictions, which can be used to guide further exploration of the system. Probabilistic surrogate models are commonly used in optimization, design, and decision-making problems in engineering, finance, and other fields.
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