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A Visual Exploration of Gaussian Processes (2019)
How to turn a collection of small building blocks into a versatile tool for solving regression problems.
Being closed under conditioning and marginalization means that the resulting distributions from these operations are also Gaussian, which makes many problems in statistics and machine learning tractable. Through the marginalization of each random variable, we can extract the respective mean function value μi′\mu’_iμi′ and standard deviation σi′=Σii′\sigma’_i = \Sigma’_{ii}σi′=Σii′ for the iii-th test point. An improved model can be created by combining the individual kernels through addition, which maintains both the periodic nature and the global ascending trend of the data.
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