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George C. Verghese. Roy, Sandip, 1978- Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-06-13T22:30:30Z 2014-06-13T22:30:30Z 2003 2003 http://hdl.handle.net/1721.1/87904 54927407 Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. Includes bibliographical references (p. 263-271). Our work is motivated by the need for tractable stochastic models for complex network and system dynamics. With this motivation in mind, we develop a class of discrete-time Markov models, called moment-linear stochastic systems (MLSS), which are structured so that moments and cross-moments of the state variables can be computed efficiently, using linear recursions. We show that MLSS provide a common framework for representing and characterizing several models that are common in the literature, such as jump-linear systems, Markov-modulated Poisson processes, and infinite server queues. We also consider MLSS models for network interactions, and hence introduce moment-linear stochastic network (MLSN) models. Several potential applications for MLSN-in such areas as traffic flow modeling, queueing, and stochastic automata modeling-are explored. Fur- ther, we exploit the quasi-linear structure of MLSS and MLSN to analyze their asymptotic dynamics, and to construct linear minimum mean-square-error estimators and minimum quadratic cost controllers. Finally, we study in detail two examples of MLSN, a stochastic automaton called the influence model and an aggregate model for air traffic flows. by Sandip Roy. Ph.D. 271 p. eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Moment-linear stochastic systems and their applications Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b92047b2-940f-4f6d-afbd-cf977e60b16a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Moment-linear stochastic systems and their applications</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2003</PublicationDate> <Authors> <Author> <DisplayName>Roy, Sandip, 1978-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Our work is motivated by the need for tractable stochastic models for complex network and system dynamics. With this motivation in mind, we develop a class of discrete-time Markov models, called moment-linear stochastic systems (MLSS), which are structured so that moments and cross-moments of the state variables can be computed efficiently, using linear recursions. We show that MLSS provide a common framework for representing and characterizing several models that are common in the literature, such as jump-linear systems, Markov-modulated Poisson processes, and infinite server queues. We also consider MLSS models for network interactions, and hence introduce moment-linear stochastic network (MLSN) models. Several potential applications for MLSN-in such areas as traffic flow modeling, queueing, and stochastic automata modeling-are explored. Fur- ther, we exploit the quasi-linear structure of MLSS and MLSN to analyze their asymptotic dynamics, and to construct linear minimum mean-square-error estimators and minimum quadratic cost controllers. Finally, we study in detail two examples of MLSN, a stochastic automaton called the influence model and an aggregate model for air traffic flows.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>