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Bayesian Parameter Estimation on Three Models of Influenza

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dc.contributor.authorTorrence, Robert Billingtonen
dc.contributor.committeechairChung, Matthiasen
dc.contributor.committeememberBorggaard, Jeffrey T.en
dc.contributor.committeememberSmith, Amber Marieen
dc.contributor.committeememberCiupe, Stanca M.en
dc.contributor.departmentMathematicsen
dc.date.accessioned2017-05-12T12:52:27Zen
dc.date.available2017-05-12T12:52:27Zen
dc.date.issued2017-05-11en
dc.description.abstractMathematical models of viral infections have been informing virology research for years. Estimating parameter values for these models can lead to understanding of biological values. This has been successful in HIV modeling for the estimation of values such as the lifetime of infected CD8 T-Cells. However, estimating these values is notoriously difficult, especially for highly complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to estimate the underlying densities of the parameters (assumed to be continuous random variables) for three models of influenza. We discuss the advantages and limitations of parameter estimation using these methods. The data and influenza models used for this project are from the lab of Dr. Amber Smith in Memphis, Tennessee.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:11582en
dc.identifier.urihttp://hdl.handle.net/10919/77611en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectInfluenzaen
dc.subjectBayesian Inferenceen
dc.subjectParameter Estimationen
dc.subjectMathematical Biologyen
dc.subjectMCMC Methodsen
dc.subjectMetropolis Algorithmen
dc.titleBayesian Parameter Estimation on Three Models of Influenzaen
dc.typeThesisen
thesis.degree.disciplineMathematicsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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