Persistent Link:
http://hdl.handle.net/10150/146860
Title:
Hidden Markov Models in Genetics and Linguistics
Author:
Keys, Kevin Lawrence
Issue Date:
May-2010
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This document provides an overview of hidden Markov Models (HMMs). It begins with some probability background, including some descriptions of algorithms used in implementing HMMs. Hidden Markov Models come from a class of systems endowed with probabilistic properties that make it useful for modeling situations in which the modeler lacks a full specification of the system in question, but has data generated by the system. This is not altogether different from a car mechanic attempting to understand an automobile motor by studying the emissions from the tailpipe and the response to acceleration, both without ever having peeked under the hood of the vehicle. To construct a theory of hidden Markov models, we first construct a theory of Markov chains; this section assumes an elementary knowledge of probability theory and univariate calculus. The subsequent two sections describe in detail two applications in particular; one in population genetics, and one in computational linguistics. This document is not meant to serve as a comprehensive text on HMMs, or on Markov models: for more technical discussion of HMMs with with many excellent examples, the reader is referred to [2].
Type:
text; Electronic Thesis
Degree Name:
B.A.
Degree Level:
bachelors
Degree Program:
Honors College; Mathematics
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleHidden Markov Models in Genetics and Linguisticsen_US
dc.creatorKeys, Kevin Lawrenceen_US
dc.contributor.authorKeys, Kevin Lawrenceen_US
dc.date.issued2010-05-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis document provides an overview of hidden Markov Models (HMMs). It begins with some probability background, including some descriptions of algorithms used in implementing HMMs. Hidden Markov Models come from a class of systems endowed with probabilistic properties that make it useful for modeling situations in which the modeler lacks a full specification of the system in question, but has data generated by the system. This is not altogether different from a car mechanic attempting to understand an automobile motor by studying the emissions from the tailpipe and the response to acceleration, both without ever having peeked under the hood of the vehicle. To construct a theory of hidden Markov models, we first construct a theory of Markov chains; this section assumes an elementary knowledge of probability theory and univariate calculus. The subsequent two sections describe in detail two applications in particular; one in population genetics, and one in computational linguistics. This document is not meant to serve as a comprehensive text on HMMs, or on Markov models: for more technical discussion of HMMs with with many excellent examples, the reader is referred to [2].en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.nameB.A.en_US
thesis.degree.levelbachelorsen_US
thesis.degree.disciplineHonors Collegeen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorUniversity of Arizonaen_US
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