A new algorithm for inference in HMM's with lower span complexity

Diogo Pereira, Cláudia Nunes and Rui Rodrigues

Computational Statistics & Data Analysis, 2024, vol. 195, issue C

Abstract: 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.

Keywords: Hidden Markov Models; Expectation-Maximization algorithm; Parallel computation; Baum-Welch algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:195:y:2024:i:c:s0167947324000392

DOI: 10.1016/j.csda.2024.107955

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