Concentration Inequalities for Dependent Random Variables on Bayesian Networks

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Yao, Rui

Advisor dc:contributor.advisor

• Daskalakis, Constantinos

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151669

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151669