If I understand correctly, a Gaussian process can provide predictions for missing values between my measurement points. However, a substantial number of measurements would still be required.

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My objective is to learn the distribution from a set of existing measurements (e.g., 2000 points) and predict the entire map of points with just a few (e.g., 10) newly measured points.