2026-09-19T00:35:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/285572022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
George C. Verghese.
Jammalamadaka, Arvind K. (Arvind Kumar), 1981-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-27T16:59:49Z
2005-09-27T16:59:49Z
2004
2004
http://hdl.handle.net/1721.1/28557
57418331
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 61-64).
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.
by Arvind K. Jammalamadaka.
S.M.
64 p.
2630181 bytes
2636206 bytes
application/pdf
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en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Aspects of inference for the Influence Model and related graphical models
Aspects of inference for the IM and related graphical models
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<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>
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