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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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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
- Language
- Released
- 2013
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- Title
- Hidden Markov Models with Applications in Computational Biology
- Subtitle
- Model Extensions and Advanced Analysis of DNA Microarray Data
- Language
- English
- Authors
- Michael Seifert
- Publisher
- 2013
- Format
- Paperback
- Pages
- 184
- ISBN13
- 9783838136042
- Category
- Nature in general
- Description
- 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.