Gaussian Process Regression From First Principles

Description

Summary

• Gaussian Process Regression is a remarkably powerful class of machine learning algorithms.
• Estimates of the mean of f(x) are produced as a linear combination of observed target values y.
• The weighting coefficients used to produce these mean estimates are independent of the target values, placing Gaussian Process Regression models into the class of linear smoothers [1].
• Photo by Siora Photography on Unsplash Covariance functions are a crucial component of GPR models, sincethese functions weight the contributions of training points to predicted test targets according to the kernel distance between observed training points X and test points X∗.
• Recall from the previous section that one way to conceptualize GPR prediction is as a linear smoothing mechanism: An example of time series mean and variance prediction using Gaussian Process Regression.