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Hidden Markov Models with Applications in Computational Biology

Model Extensions and Advanced Analysis of DNA Microarray Data

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  • 184pages
  • 7 heures de lecture

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Higher-order Hidden Markov Models (HMMs) serve as powerful extensions of their first-order counterparts, with applications in fields like speech recognition, image segmentation, and computational biology. This book offers a comprehensive introduction to the algorithmic foundations of higher-order HMMs, including novel extensions such as parsimonious models and those with scaled transition matrices. It also explores practical applications of these models in analyzing DNA microarray datasets, making it a valuable resource for readers familiar with first-order HMMs seeking deeper insights.

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Hidden Markov Models with Applications in Computational Biology, Michael Seifert

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Année de publication
2013
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