Thesis Title

Hidden Markov Models and Some Applications in Bioinformatics

Date of Graduation

Summer 2003

Degree

Master of Science in Mathematics

Department

Mathematics

Committee Chair

George Mathew

Subject Categories

Mathematics

Abstract

Hidden Markov Models are probabilistic functions of finite state Markov chains. At each state of a Markov chain a symbol is emitted. In many processes, the sequence of emitted symbols are observed, and the states of the processes are hidden or unknown. For analyzing such a data, it becomes necessary to determine the probability for obtaining such a sequence of observed symbols. Also, one would like to determine the most likely sequence of states which produced the observed symbols. To solve these problems, the forward algorithm, the backward algorithm and the Viterbi algorithm are introduced. A survey of some of the results for these algorithms, and a survey of some of their applications in bioinformatics are presented. Also, proofs of some of the results are provided.

Copyright

© Maryam Alyasin Arvizu

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