{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/28557"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/28557","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Aspects of inference for the Influence Model and related graphical models","abstract":"The Influence Model (IM), developed with the primary motivation of describing network dynamics in power systems, has proved to be very useful in a variety of contexts. It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major goals of this thesis are (1) to place the Influence Model in the broader framework of graphical models, such as Bayesian networks, (2) to provide and discuss a hybrid model between the IM and dynamic Bayesian networks, (3) to discuss the use of inference tools available for such graphical models in the context of the IM, and (4) to provide some methods of estimating the unknown parameters that describe the IM. We hope each of these developments will enhance the use of IM as a tool for studying networked interact ions.","abstract_html":"The Influence Model (IM), developed with the primary motivation of describing network dynamics in power systems, has proved to be very useful in a variety of contexts. It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major goals of this thesis are (1) to place the Influence Model in the broader framework of graphical models, such as Bayesian networks, (2) to provide and discuss a hybrid model between the IM and dynamic Bayesian networks, (3) to discuss the use of inference tools available for such graphical models in the context of the IM, and (4) to provide some methods of estimating the unknown parameters that describe the IM. We hope each of these developments will enhance the use of IM as a tool for studying networked interact ions.","abstract_has_math":false,"creators":["Jammalamadaka, Arvind K. (Arvind Kumar), 1981-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["George C. Verghese."],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004","date_published":"2004","updated_at":"2026-07-22T22:21:22Z","subjects":["Electrical Engineering and Computer Science."],"languages":["en_US"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/28557","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["George C. Verghese."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major goals of this thesis are (1) to place the Influence Model in the broader framework of graphical models, such as Bayesian networks, (2) to provide and discuss a hybrid model between the IM and dynamic Bayesian networks, (3) to discuss the use of inference tools available for such graphical models in the context of the IM, and (4) to provide some methods of estimating the unknown parameters that describe the IM. We hope each of these developments will enhance the use of IM as a tool for studying networked interact ions."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Aspects of inference for the Influence Model and related graphical models"]}]}],"canonical_facts":{"dc:contributor.advisor":["George C. Verghese."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Jammalamadaka, Arvind K. (Arvind Kumar), 1981-"],"dc:date.accessioned":["2005-09-27T16:59:49Z"],"dc:date.available":["2005-09-27T16:59:49Z"],"dc:date.issued":["2004"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.","Includes bibliographical references (p. 61-64)."],"dc:description.abstract":["The Influence Model (IM), developed with the primary motivation of describing network dynamics in power systems, has proved to be very useful in a variety of contexts. It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major goals of this thesis are (1) to place the Influence Model in the broader framework of graphical models, such as Bayesian networks, (2) to provide and discuss a hybrid model between the IM and dynamic Bayesian networks, (3) to discuss the use of inference tools available for such graphical models in the context of the IM, and (4) to provide some methods of estimating the unknown parameters that describe the IM. We hope each of these developments will enhance the use of IM as a tool for studying networked interact ions."],"dc:description.degree":["S.M."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/28557"],"dc:language.iso":["en_US"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Aspects of inference for the Influence Model and related graphical models"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:22Z"}