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
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.
Subject Categories
Mathematics
Copyright
© Maryam Alyasin Arvizu
Recommended Citation
Arvizu, Maryam Alyasin, "Hidden Markov Models and Some Applications in Bioinformatics" (2003). MSU Graduate Theses. 1624.
https://bearworks.missouristate.edu/theses/1624
Dissertation/Thesis
