Implementation of Gaussian process using numpy. This repository implements Gaussian processes as described in [1]
Samples are drawn from the prior distribution without any training data and with initial hyperparameters
Adding training samples drawn from f(z) = z^2. The Gaussian process is able to create prediction but the selected hyperparameters are not ideal.
Using a negative log likelyhood minimization, the hyperparameters are fitted to the training data, producing more reliable predictions
[1] Rasmussen, C.E. and Williams, C.K.I. (2005). Gaussian Processes for Machine Learning