{"id":{"repo_id":"arizona-thes","oai_identifier":"oai:repository.arizona.edu:10150/656830"},"canonical_url":"https://search.dev.ndltd.org/etd/arizona-thes/oai:repository.arizona.edu:10150/656830","repository":{"repo_id":"arizona-thes","name":"University of Arizona","base_url":"https://repository.arizona.edu/oai/request"},"display":{"title":"Probabilistic Graphical Models for Crowdsourcing and Turbulence","abstract":"Graphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and usefulness of graphical models on two problems, one involving crowdsourcing and one involving turbulence. In crowdsourcing, we consider the problem of inferring true labels from a set of crowdsourced annotations. We design generative models for the crowdsourced annotations involving as latent variables the worker reliability, the structure of the labels, and the ground truth labels. Furthermore, we design an effective inference algorithm to infer the latent variables. In turbulence, we consider the problem of modeling the mixing distribution of homogeneous isotropic passive scalar turbulence. We consider models specifying the conditional distribution of a coarse grained node given its adjacent coarse grained nodes. In particular, we demonstrate the effectiveness of a higher order moments based extension of the Gaussian distribution.","abstract_html":"Graphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and usefulness of graphical models on two problems, one involving crowdsourcing and one involving turbulence. In crowdsourcing, we consider the problem of inferring true labels from a set of crowdsourced annotations. We design generative models for the crowdsourced annotations involving as latent variables the worker reliability, the structure of the labels, and the ground truth labels. Furthermore, we design an effective inference algorithm to infer the latent variables. In turbulence, we consider the problem of modeling the mixing distribution of homogeneous isotropic passive scalar turbulence. We consider models specifying the conditional distribution of a coarse grained node given its adjacent coarse grained nodes. In particular, we demonstrate the effectiveness of a higher order moments based extension of the Gaussian distribution.","abstract_has_math":false,"creators":["Luo, Zhaorui"],"institution":"The University of Arizona.","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":"Graduate College","degree_department":null,"school":null,"contributors":[],"advisors":["Yin, Junming"],"committee_chairs":[],"committee_members":["Watkins, Joseph C.","Zhang, Hao","Chertkov, Michael"],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-24T00:57:10Z","subjects":[],"languages":["en"],"rights":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10150/656830","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Yin, Junming"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Watkins, Joseph C.","Zhang, Hao","Chertkov, Michael"]},{"key":"dc:creator","label":"Author","values":["Luo, Zhaorui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-20T02:32:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-20T02:32:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:publisher","label":"Institution","values":["The University of Arizona."]},{"key":"dc:type","label":"Dc Type","values":["text","Electronic Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Graduate College","Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Arizona"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10150/656830"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Graphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and usefulness of graphical models on two problems, one involving crowdsourcing and one involving turbulence. In crowdsourcing, we consider the problem of inferring true labels from a set of crowdsourced annotations. We design generative models for the crowdsourced annotations involving as latent variables the worker reliability, the structure of the labels, and the ground truth labels. Furthermore, we design an effective inference algorithm to infer the latent variables. In turbulence, we consider the problem of modeling the mixing distribution of homogeneous isotropic passive scalar turbulence. We consider models specifying the conditional distribution of a coarse grained node given its adjacent coarse grained nodes. In particular, we demonstrate the effectiveness of a higher order moments based extension of the Gaussian distribution."]},{"key":"dc:title","label":"Title","values":["Probabilistic Graphical Models for Crowdsourcing and Turbulence"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yin, Junming"],"dc:contributor.committeemember":["Watkins, Joseph C.","Zhang, Hao","Chertkov, Michael"],"dc:creator":["Luo, Zhaorui"],"dc:date.accessioned":["2021-02-20T02:32:07Z"],"dc:date.available":["2021-02-20T02:32:07Z"],"dc:date.issued":["2020"],"dc:description.abstract":["Graphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and usefulness of graphical models on two problems, one involving crowdsourcing and one involving turbulence. In crowdsourcing, we consider the problem of inferring true labels from a set of crowdsourced annotations. We design generative models for the crowdsourced annotations involving as latent variables the worker reliability, the structure of the labels, and the ground truth labels. Furthermore, we design an effective inference algorithm to infer the latent variables. In turbulence, we consider the problem of modeling the mixing distribution of homogeneous isotropic passive scalar turbulence. We consider models specifying the conditional distribution of a coarse grained node given its adjacent coarse grained nodes. In particular, we demonstrate the effectiveness of a higher order moments based extension of the Gaussian distribution."],"dc:identifier.uri":["http://hdl.handle.net/10150/656830"],"dc:language.iso":["en"],"dc:publisher":["The University of Arizona."],"dc:rights":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author."],"dc:title":["Probabilistic Graphical Models for Crowdsourcing and Turbulence"],"dc:type":["text","Electronic Dissertation"],"thesis:degree_discipline":["Graduate College","Mathematics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Arizona"]},"updated_at":"2026-07-24T00:57:10Z"}