{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105227"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105227","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian regularization for graphical models and variants: Theory and algorithms","abstract":"This Dissertation was approved for publication on 2019-04-19 at 09:57.","abstract_html":"This Dissertation was approved for publication on 2019-04-19 at 09:57.","abstract_has_math":false,"creators":["Gan, Lingrui"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Liang, Feng","Narisetty, Naveen Naidu","Qu, Annie","Chen, Xiaohui"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:48:19Z","date_published":"2019-08-23T20:48:19Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Bayesian Regularization","Spike and Slab Priors","Graphical Models","High Dimensional Estimation","Scalable Computation"],"languages":["en"],"rights":["Copyright 2019 Lingrui Gan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105227","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Feng","Narisetty, Naveen Naidu","Qu, Annie","Chen, Xiaohui"]},{"key":"dc:creator","label":"Author","values":["Gan, Lingrui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:19Z","2021-08-24T09:15:24Z","2019-04-19","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bayesian Regularization","Spike and Slab Priors","Graphical Models","High Dimensional Estimation","Scalable Computation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Lingrui Gan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105227"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Dissertation was approved for publication on 2019-04-19 at 09:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13757 on 2019-08-22 at 16:23:18","Made available in DSpace on 2019-08-23T20:48:19Z (GMT). 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The methods and theoretical results developed in the thesis are applicable for many commonly used high dimensional models, with a particular emphasis on graphical models and conditional random fields using the spike and slab Lasso regularization which is a special case of our general Bayesian regularization framework. We propose fast and scalable EM algorithms for computing the maximum a posterior (MAP) estimators and (approximate) posterior probabilities for support recovery. 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No. of bitstreams: 3 GAN-DISSERTATION-2019.pdf: 3173113 bytes, checksum: f30011a82abe0eee6f12175d3c1540a8 (MD5) LICENSE.txt: 4208 bytes, checksum: 7e78d780295f0dff593175f522bff538 (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: 9421c428a2502adec76cccd47490ba85 (MD5) Previous issue date: 2019-04-19","Embargo set by: Seth Robbins for item 112349 Lift date: 2021-08-23T20:48:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 112349 on 2021-08-24T09:15:24Z.","The Bayesian framework offers a flexible tool for regularization in the high dimensional setting. In this thesis, I propose a new class of Bayesian regularization methods induced from scale mixtures of Laplace prior distributions and develop novel statistical methods for a variety of statistical models. We provide theoretical guarantees of our methods (both in estimation accuracy and structure recovery) that are stronger than the existing results. The methods and theoretical results developed in the thesis are applicable for many commonly used high dimensional models, with a particular emphasis on graphical models and conditional random fields using the spike and slab Lasso regularization which is a special case of our general Bayesian regularization framework. We propose fast and scalable EM algorithms for computing the maximum a posterior (MAP) estimators and (approximate) posterior probabilities for support recovery. Extensive empirical results on synthetic and real datasets demonstrate that the proposed methods have merits when compared to the alternative methods.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Lingrui Gan, accepted the attached license on 2019-04-18 at 16:44.","The student, Lingrui Gan, submitted this Dissertation for approval on 2019-04-18 at 18:23."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105227"],"dc:language":["en"],"dc:rights":["Copyright 2019 Lingrui Gan"],"dc:subject":["Bayesian Regularization","Spike and Slab Priors","Graphical Models","High Dimensional Estimation","Scalable Computation"],"dc:title":["Bayesian regularization for graphical models and variants: Theory and algorithms"],"dc:type":["text"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}