A Concise Information-Theoretic Derivation of the Baum-Welch algorithm
Information Theory
2014-06-27 v1 Machine Learning
math.IT
Abstract
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.
Keywords
Cite
@article{arxiv.1406.7002,
title = {A Concise Information-Theoretic Derivation of the Baum-Welch algorithm},
author = {Alireza Nejati and Charles Unsworth},
journal= {arXiv preprint arXiv:1406.7002},
year = {2014}
}