User Guide

• 1. Supervised learning
  • 1.1. Generalized Linear Models
    • 1.1.1. Bayesian Linear Regression
    • 1.1.2. Bayesian Logistic regression
  • 1.2. Gaussian Processes
    • 1.2.1. Gaussian Process Regression (GPR)
    • 1.2.2. Kernels for Gaussian Processes
      • 1.2.2.1. References
  • 1.3. Naive Bayes
    • 1.3.1. Gaussian Naive Bayes
  • 1.4. Neural network models (supervised)
    • 1.4.1. Multi-layer Perceptron
    • 1.4.2. Classification
• 2. Unsupervised learning
  • 2.1. Gaussian mixture models
    • 2.1.1. Gaussian Mixture
    • 2.1.2. The Dirichlet Process