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A new algorithm for inference in HMM's with lower span complexity
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| 1.13 MB | Adobe PDF |
Orientador(es)
Resumo(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.
Descrição
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© 2024 The Author(s)
Palavras-chave
Baum-Welch algorithm Expectation-Maximization algorithm Hidden Markov Models Parallel computation Statistics and Probability Computational Mathematics Computational Theory and Mathematics Applied Mathematics