Gaussian Process Regression (GPR) | Python
Gaussian Process (GP) Regression with Python
Draw sample functions from GP prior distribution.
Draw sample functions from GP posterior distribution, given the training data points.
Vary the kernel parameters to see the impact on the prediction uncertainty
Vary the noise variance to see the impact on the prediction uncertainty
How observing new datapoints reduce uncertainty in prediction with GP
#python #gaussianprocss #machinelearning #regression #bayesian
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