A Concise Information-Theoretic Derivation of the Baum-Welch algorithm
arXiv:1406.7002v11 citations
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This work provides an incremental improvement in the theoretical understanding of HMMs for researchers in machine learning and statistics.
The paper tackled the problem of deriving the Baum-Welch algorithm for hidden Markov models by using an information-theoretic approach based on cross-entropy, resulting in a more concise derivation that naturally generalizes to multiple observations.
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.