Table of Contents


  1. Introduction
    1. Discrete Probability
  2. Graphical Models
    1. Bayesian Networks
    2. Markov Random Fields
  3. Exact Inference
    1. Variable Elimination
    2. Belief Propagation
  4. Approximate Inference
    1. Approximate MAP Inference
    2. Variational Inference
    3. Naive Mean Field
  5. Sampling
    1. Importance Sampling
    2. Markov Chain Monte Carlo (MCMC) Methods
  6. Machine Learning
    1. Maximum Likelihood Estimation (MLE)
    2. Maximum Entropy
    3. Pseudolikelihoods
    4. Expectation Maximization (EM)
  7. Continuous State Spaces
    1. Exponential Families
    2. Latent Dirichlet Allocation (LDA)
    3. Mixture Models
    4. Expectation Propagation (EP)
Creative Commons License