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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