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1 - 3 of 3 results for: MS&E 221: Stochastic Modeling

MS&E 221: Stochastic Modeling

Focus is on time-dependent random phenomena. Topics: discrete time Markov chains, Markov jump processes, queueing theory, and applications. Emphasis on model-building, computation, and related calibration and statistical issues. Prerequisite: 220 or equivalent, or consent of instructor.
Terms: Spr | Units: 3

MS&E 334: Topics in Social Data

In-depth survey of methods for the analysis of large-scale social and behavioral data. Particular focus on recent developments in preference learning. Connections made to graph-theoretic investigations common in the study of social networks. Topics include discrete choice theory, random utility models, item-response theory, rank aggregation, centrality and ranking on graphs, and random graph models of social networks. Intended for Ph.D. students, but masters students with adequate background and interest in research topics are welcome to apply. Strongly recommended: 200-level courses in stochastic modeling (most specifically, Markov chains), optimization, and machine learning (e.g., MS&E 211, 221, 226, and CS161 or equivalents). Limited enrollment. Please complete the application here: https://forms.gle/MWJaehpMKc2hrrU27
Terms: Spr | Units: 3

MS&E 335: Queueing and Scheduling in Processing Networks

Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.
Last offered: Autumn 2020
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