Baum-Welch algorithm

(45 minutes to learn)


The Baum-Welch algorithm is an algorithm for maximum likelihood learning in hidden Markov models (HMMs). It is a special case of expectation-maximization (EM), and alternates between inferring the posterior marginals and maximizing the expected log-likelihood given those posterior marginals.


This concept has the prerequisites:


  • Derive the Baum-Welch algorithm as a special case of EM.

Core resources (read/watch one of the following)


See also

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