A Gaussian process is a collection of random variables, any finite number of which have a joint Gaussian distribution. It is characterized by its mean function and covariance function, which determine the shape and smoothness of the process. This property makes Gaussian processes powerful tools for modeling and inference in various fields, allowing for predictions with uncertainty quantification.
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