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Więcej informacji o Hidden Markov Models with Applications in Computational Biology

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Opis

Standard first-order Hidden Markov Models (HMMs) are very popular tools for the analysis of sequential data in applied sciences. HMMs are versatile and structurally simple models enabling probabilistic modeling based on a sound theoretical grounding. In contrast to the broad usage of first-order HMMs, applications of higher-order HMMs are very rare, but they have been proven to be powerful extensions of first-order HMMs including applications in speech recognition, image segmentation or computational biology. This book provides the first easily accessible and comprehensive extension of the algorithmic basics of first-order HMMs to higher-order HMMs coupled with practical applications in computational biology. The book starts with a theoretical part developing the algorithmic basics of higher-order HMMs and two novel model extensions (i) parsimonious higher-order HMMs and (ii) HMMs with scaled transition matrices. The second part considers applications of these models to the analysis of different DNA microarray data sets followed by a detailed discussion. The book addresses readers having basic knowledge on first-order HMMs interested to gain more insights on higher-order HMMs.

Szczegóły książki

Kategoria Książki po angielsku Mathematics & science Mathematics

• Pełny tytuł: Hidden Markov Models with Applications in Computational Biology
• Podtytuł: Model Extensions and Advanced Analysis of DNA Microarray Data
• Autor: Michael Seifert
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 184
• EAN: 9783838136042
• ISBN: 3838136047
• ID: 07088039
• Wydawca: Sudwestdeutscher Verlag Fur Hochschulschriften AG
• Waga: 277 g
• Wymiary: 229 × 152 × 11 mm
• Data wydania: 02. January 2013

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