A Concise Information-Theoretic Derivation of the Baum-Welch algorithm
  We derive the Baum-Welch algorithm for hidden Markov models (HMMs) through an information-theoretical approach using cross-entropy instead of the Lagrange multiplier approach which is universal in machine learning literature. The proposed approach provides a more concise derivation of the Baum-Welch method and naturally generalizes to multiple observations. 
Alireza Nejati, Charles Unsworth
cs.IT cs.LG math.IT