Bayesian Graphical Models for Multiple Networks

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dc.contributor.advisorVannucci, Marinaen_US
dc.creatorShaddox, Elin Brookeen_US
dc.date.accessioned2019-05-17T19:04:15Zen_US
dc.date.available2020-05-01T05:01:09Zen_US
dc.date.created2019-05en_US
dc.date.issued2019-04-16en_US
dc.date.submittedMay 2019en_US
dc.date.updated2019-05-17T19:04:15Zen_US
dc.description.abstractIn recent years, novel methods for graphical model inference have been widely applied to infer biological networks for high throughput data. When studying complex diseases, these network-based inferential approaches can be crucial in evaluating patterns of variable association and determining the cellular level changes influencing disease susceptibility, progression, and variation across heterogeneous subjects. This thesis is focused on developing flexible joint graph methodology for estimating network structures of multiple sample groups. These approaches employ Gaussian graphical models, Markov random field priors, continuous shrinkage priors, and Dirichlet process priors to infer graph structures for each sample group, while sharing information between related graphs without assuming any similarity. These methods are illustrated through simulation studies and applications to case studies of Chronic Obstructive Pulmonary Disease (COPD).en_US
dc.embargo.terms2020-05-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationShaddox, Elin Brooke. "Bayesian Graphical Models for Multiple Networks." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/105996">https://hdl.handle.net/1911/105996</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/105996en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectBayesian graphical modelen_US
dc.subjectCOPDen_US
dc.subjectmarkov random field prioren_US
dc.subjectspike and slab prioren_US
dc.subjectjoint graphen_US
dc.subjectmultiple graphen_US
dc.titleBayesian Graphical Models for Multiple Networksen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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