Pereira, DiogoNunes, CláudiaRodrigues, Rui2025-02-102025-02-102024-070167-9473PURE: 106515953PURE UUID: 57277d4d-92fb-401f-bbcb-3eaf08ea1d73Scopus: 85188454757http://hdl.handle.net/10362/178751Publisher Copyright: © 2024 The Author(s)The maximum likelihood problem for Hidden Markov Models is usually numerically solved by the Baum-Welch algorithm, which uses the Expectation-Maximization algorithm to find the estimates of the parameters. This algorithm has a recursion depth equal to the data sample size and cannot be computed in parallel, which limits the use of modern GPUs to speed up computation time. A new algorithm is proposed that provides the same estimates as the Baum-Welch algorithm, requiring about the same number of iterations, but is designed in such a way that it can be parallelized. As a consequence, it leads to a significant reduction in the computation time. This reduction is illustrated by means of numerical examples, where we consider simulated data as well as real datasets.1189986engBaum-Welch algorithmExpectation-Maximization algorithmHidden Markov ModelsParallel computationStatistics and ProbabilityComputational MathematicsComputational Theory and MathematicsApplied Mathematicsjournal article10.1016/j.csda.2024.107955https://www.scopus.com/pages/publications/85188454757