Concentration Inequalities for Dependent Random
Variables on Bayesian Networks
Name
yao-rayyao-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
475.13 KB
Format
Adobe PDF
Checksum (MD5)
7b2c137fc526bda247503c9a03a12b03
Author(s)
Yao, Rui
Advisor(s)
Daskalakis, Constantinos
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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