Probabilistic graphical models

Bayesian networks and their Markov properties, structure scores and Markov equivalence, causal networks and adjustment, belief propagation and variable elimination, conditional random fields, hidden Markov models (forward-backward, Viterbi, Baum-Welch) and the Kalman filter.

• Hidden Markov models
• Baum–Welch algorithm
• Conditional random fields
• Kalman filter
• Markov equivalence and Bayesian network scores
• Bayesian networks
• Causal Bayesian networks
• Variable elimination and belief propagation

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