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A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information. (arXiv:2201.11023v1 [math.ST])
Jan. 27, 2022, 2:10 a.m. | John Nicholson, Peter Kiessler, D. Andrew Brown
stat.ML updates on arXiv.org arxiv.org
Gaussian processes are among the most useful tools in modeling continuous
processes in machine learning and statistics. If the value of a process is
known at a finite collection of points, one may use Gaussian processes to
construct a surface which interpolates these values to be used for prediction
and uncertainty quantification in other locations. However, it is not always
the case that the available information is in the form of a finite collection
of points. For example, boundary value …
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