GP stands for Gaussian Process, which is a powerful and flexible statistical model used for regression, classification, and probabilistic modeling tasks. It belongs to the family of Bayesian non-parametric methods and is particularly useful where prior beliefs about the underlying function to be encoded into the model.

The choice of kernel (covariance function) defines the assumptions about the smoothness and behavior of the underlying function. Popular kernels include RBF (Gaussian), Matérn, and linear kernels.

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