Scalable inference for Bayesian non-parametrics

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dc.contributor.advisorWilliamson, Sinead
dc.contributor.committeeMemberMueller, Peter
dc.contributor.committeeMemberScott, James G
dc.contributor.committeeMemberXing, Eric P
dc.creatorZhang, Michael Minyi
dc.date.accessioned2018-07-23T16:42:01Z
dc.date.available2018-07-23T16:42:01Z
dc.date.created2018-05
dc.date.issued2018-06-25
dc.date.submittedMay 2018
dc.date.updated2018-07-23T16:42:01Z
dc.description.abstractBayesian non-parametric models, despite their theoretical elegance, face a serious computational burden that prevents their use in serious "big data'' scenarios. Furthermore, we cannot expect the data in "big data'' to exist solely on one processor, so we must have parallel algorithms that are valid Bayesian inference samplers. However, inherent dependencies in Bayesian non-parametric models make this task very difficult. Instead, we must either construct good approximations or develop clever reformulations of our models so that we perform inference with provably accurate results. This thesis will discuss four methods developed to parallelize inference in the Bayesian and Bayesian non-parametric setting.
dc.description.departmentStatistics
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T29C6SJ4J
dc.identifier.urihttp://hdl.handle.net/2152/65734
dc.subjectBayesian non-parametrics
dc.subjectScalable inference
dc.subjectMachine learning
dc.titleScalable inference for Bayesian non-parametrics
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentStatistics
thesis.degree.disciplineStatistics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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