Computational Bayesian methods applied to complex problems in bio and astro statistics.

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dc.contributor.advisorHejduk, Matthew Dickerson, 1967-
dc.creatorElrod, Chris Michael, 1991-
dc.date.accessioned2020-02-19T16:53:30Z
dc.date.available2020-02-19T16:53:30Z
dc.date.created2019-12
dc.date.issued2019-09-05
dc.date.submittedDecember 2019
dc.date.updated2020-02-19T16:53:30Z
dc.description.abstractIn this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in simulation to analyze how estimates of a parameter of interest change across sample sizes.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2104/10803
dc.language.isoen
dc.rights.accessrightsWorldwide access.
dc.rights.accessrightsAccess changed 5/23/22.
dc.subjectTBD. Bayesian Normal-Inverse-Wishart model. Bayesian methods. Hamiltonian Monte Carlo.
dc.titleComputational Bayesian methods applied to complex problems in bio and astro statistics.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2021-12-01
local.embargo.terms2021-12-01
thesis.degree.departmentBaylor University. Dept. of Statistical Science.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

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