Bayesian Learning 2: EM Algorithm

Points covered:

• Revision of Lagrange multipliers
• Maximizing probabilities using Lagrange multipliers
• The EM Theorem
• Derivation of the Baum-Welch Algorithm
• Learning the parameters of a HMM
• Estimating the means of k Gaussians