{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/113125"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/113125","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Variational inference for non-stationary distributions","abstract":"In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question.","abstract_html":"In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question.","abstract_has_math":false,"creators":["Mamikonyan, Arsen"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Samuel Madden."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:30Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/113125","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Samuel Madden."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Mamikonyan, Arsen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-12T20:57:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-12T20:57:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/113125"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (page 49)."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Variational inference for non-stationary distributions"]}]}],"canonical_facts":{"dc:contributor.advisor":["Samuel Madden."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Mamikonyan, Arsen"],"dc:date.accessioned":["2018-01-12T20:57:47Z"],"dc:date.available":["2018-01-12T20:57:47Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (page 49)."],"dc:description.abstract":["In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/113125"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Variational inference for non-stationary distributions"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:30Z"}