Empirical Priors for High-dimensional Structure Learning Problems.

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dc.contributor.advisorRyan Martin, Co-Chair
dc.contributor.advisorHoward Bondell, Co-Chair
dc.contributor.advisorSubhashis Ghoshal, Member
dc.contributor.advisorBrian Reich, Member
dc.contributor.advisorArion Midgett, Graduate School Representative
dc.contributor.authorLiu, Chang
dc.date.accepted2020-07-20
dc.date.accessioned2020-09-05T12:30:37Z
dc.date.available2020-09-05T12:30:37Z
dc.date.defense2020-07-14
dc.date.issued2020-07-14
dc.date.released2020-09-05
dc.date.reviewed2020-07-16
dc.date.submitted2020-07-16
dc.degree.disciplineStatistics
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Statistics.
dc.formatPh.D. North Carolina State University, 2020.
dc.identifier.otherdeg22344
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/38259
dc.titleEmpirical Priors for High-dimensional Structure Learning Problems.
dcterms.extent1 online resource (ix, 127 pages) : illustrations

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