Probabilistic Graphical Models

Implementations written during the PGM class at UdeM (ift6269):

  • some classifiers: Fisher LDA, logistic regression with IRLS, QDA
  • some clustering algorithms: k-means, Gaussian mixtures
  • a Hidden Markov Model with inference and learning algorithms (Baum-Welch, Viterbi decoding etc..)
  • approximate inference in a simple Ising model (Gibbs sampling and variational inference)