2026-09-18T23:43:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1516692023-08-01T03:10:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daskalakis, Constantinos
Yao, Rui
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:57:46Z
2023-07-31T19:57:46Z
2023-06
2023-06-06T16:35:26.124Z
https://hdl.handle.net/1721.1/151669
The thesis presents a theoretical study of the concentration results for the function defined on the random variables on a Bayesian Network. In this work, we provide several concentration inequality results under the assumption that the function is Lipshitz or bounded difference. In addition, we illustrate about the concentration of the maximum likelihood estimator of some learning models. We also show the optimality of certain results and the comparison to the results in other relevant literature.
M.Eng.
Massachusetts Institute of Technology
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Concentration Inequalities for Dependent Random
Variables on Bayesian Networks
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>The thesis presents a theoretical study of the concentration results for the function defined on the random variables on a Bayesian Network. In this work, we provide several concentration inequality results under the assumption that the function is Lipshitz or bounded difference. In addition, we illustrate about the concentration of the maximum likelihood estimator of some learning models. We also show the optimality of certain results and the comparison to the results in other relevant literature.</Abstract>
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