Some Illustration of Gaussian Processes with simulated data. The project is part of a seminar paper in the course "Machine Learning - A Probabilistic Approach" at Humboldt University
See individual folders for code descriptions and useful sources.
Author: Clara Hoffmann
One-dimensional Gaussian process with noiseless and noisy samples of a sine function and conjugate gradient optimization. partly based on Figure 2.5 in "Gaussian Processes for Machine Learning" by Rasmussen & Williams (2006) and Figure 15.3 in "Machine Learning - A Probabilistic Perspective" by Kevin P. Murphy (2012)
One-dimensional Gaussian process with squared exponential kernel and varying hyperparameters,extended representation of figure 5.5 from Rasmussen & Williams "Gaussian Processes for Machine Learning"
One-dimensional Gaussian process with different kernels.
Development of the log-likelihood of a one-dimensional Gaussian process with varying length scales and hyperparameters (replicates Figure 5.3 from Rasmussen & Williams "Gaussian Processes for Machine Learning")
