Hidden Markov Models with Applications in Computational Biology
Hidden Markov Models with Applications in Computational Biology

Hidden Markov Models with Applications in Computational Biology


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About the Book

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.


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Product Details
  • ISBN-13: 9783838136042
  • Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 11 mm
  • Width: 152 mm
  • ISBN-10: 3838136047
  • Publisher Date: 02 Jan 2013
  • Height: 229 mm
  • No of Pages: 184
  • Series Title: English
  • Weight: 326 gr


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Hidden Markov Models with Applications in Computational Biology
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Hidden Markov Models with Applications in Computational Biology
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