Tutorials

• A gentle introduction to Gaussian Process Regression
• Model fitting with correlated noise
  • A Simple Mean Model
  • Simulated Dataset
  • Assuming White Noise
  • Modeling the Noise
  • The Final Fit
• Hyperparameter optimization
  • Optimization
  • Sampling & Marginalization
• Scaling Gaussian Processes to big datasets
• Implementing new kernels
  • The kernel function
  • Kernel specification
• Mixtures of GPs
• Bayesian optimization