Probabilistic Analysis of Distributed Processes with Focus on Consensus

Resource type
Thesis type
(Thesis) Ph.D.
Date created
2017-09-22
Authors/Contributors
Abstract
This thesis is devoted to the study of stochastic decentralized processes. Typical examples in the real world include the dynamics of weather and temperature, of traffic, the way we meet our friends, etc. We take the rich tool set from probability theory for the analysis of Markov Chains and employ it to study a wide range of such distributed processes: Forest Fire Model (social networks), Balls-into-Bins with Deleting Bins, and fundamental consensus dynamics and protocols such as the Voter Model, 2-Choices, and 3-Majority.
Document
Identifier
etd10387
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Copyright is held by the author.
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This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Berenbrink, Petra
Thesis advisor: Mathieu, Claire
Member of collection
Attachment Size
etd10387_FMallmann-Trenn.pdf 2.23 MB