@MastersThesis{Son01,
  author =       {S{\"o}ren Sonnenburg},
  title =        {Hidden Markov Model for Genome Analysis},
  school =       {Humboldt University},
  year =         {2001},
  type =         {Project Report},
  ps =           {http://sonnenburgs.de/soeren/publications/Son01.ps.gz},
  pdf =          {http://sonnenburgs.de/soeren/publications/Son01.pdf.gz},
  abstract = {
  A Hidden Markov Model (HMM) is a Markov chain, where each state generates an observation.
  One only sees the observations, and the goal is to infer the \emph{hidden} state sequence.
  HMMs have been applied successfully e.g.~to Speech Recognition tasks
  and to biological sequence analysis. They have recently been used
  for gene finding and for identifying homologous sequences.  The aim
  of our work was to create a reliable, flexible and efficient tool
  that can be used for \emph{genome analysis}. Our implementation
  exhibits some particularly interesting features, such as Viterbi and
  Baum-Welch training algorithms, customizable model structures and
  support for higher order Hidden Markov models. It has been particularly
  tuned for large numbers of observations. To speed-up the
  computations it also supports multi-processor systems. By this work
  we have created the basis for developing more sophisticated algorithms
  for genome analysis in subsequent projects.
  }
}