{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/77611"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/77611","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Bayesian Parameter Estimation on Three Models of Influenza","abstract":"Mathematical 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.","abstract_html":"Mathematical 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.","abstract_has_math":false,"creators":["Torrence, Robert Billington"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mathematics","degree_department":"Mathematics","school":null,"contributors":[],"advisors":[],"committee_chairs":["Chung, Matthias"],"committee_members":["Borggaard, Jeffrey T.","Smith, Amber Marie","Ciupe, Stanca M."],"year":2017,"date_issued":"2017-05-11","date_published":"2017-05-11","updated_at":"2026-07-22T22:19:49Z","subjects":["Influenza","Bayesian Inference","Parameter Estimation","Mathematical Biology","MCMC Methods","Metropolis Algorithm"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:11582"],"render_values":[{"text":"vt_gsexam:11582","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/77611","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Chung, Matthias"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Borggaard, Jeffrey T.","Smith, Amber Marie","Ciupe, Stanca M."]},{"key":"dc:contributor.department","label":"Department","values":["Mathematics"]},{"key":"dc:creator","label":"Author","values":["Torrence, Robert Billington"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-05-12T12:52:27Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-05-12T12:52:27Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-05-11"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Influenza","Bayesian Inference","Parameter Estimation","Mathematical Biology","MCMC Methods","Metropolis Algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:11582"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/77611"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Mathematical 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."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Mathematical 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 perform parameter estimation for three models of influenza. We discuss the advantages and limitations of these methods. The data and influenza models used for this project are from the lab of Dr. Amber Smith in Memphis, Tennessee."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Bayesian Parameter Estimation on Three Models of Influenza"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Chung, Matthias"],"dc:contributor.committeemember":["Borggaard, Jeffrey T.","Smith, Amber Marie","Ciupe, Stanca M."],"dc:contributor.department":["Mathematics"],"dc:creator":["Torrence, Robert Billington"],"dc:date.accessioned":["2017-05-12T12:52:27Z"],"dc:date.available":["2017-05-12T12:52:27Z"],"dc:date.issued":["2017-05-11"],"dc:description.abstract":["Mathematical models of viral infections have been informing virology research for years. Estimating parameter values for these models can lead to understanding of biological values. 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However, estimating these values is notoriously difficult, especially for highly complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to perform parameter estimation for three models of influenza. We discuss the advantages and limitations of these methods. 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