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Gaussian Process Kernels are covariance functions used in Gaussian processes to measure the relationships among data points, such as in a time series. These kernels generate matrices that capture the intra-data relationship, allowing the Gaussian Process to make projections or forecasts by assuming the data follows a normal distribution. As these series look to explore new ideas while also examining how these ideas can be exploited, Gaussian Process (GP) Kernels are serving as our subject in building a custom signal.

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