Gaussian Processes

Gaussian Processes (GP) are a family of probabilistic models that can be used for regression and classification tasks. In GP, a function is represented as a probability distribution over functions, where any finite set of function values has a joint Gaussian distribution. This allows for flexible modeling of complex functions and uncertainty quantification. GPs are commonly used in Bayesian optimization, where the goal is to find the minimum or maximum of an unknown function by iteratively selecting points to evaluate. They are also used in spatial statistics, time series analysis, and computer vision.
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