Double coset Markov chains

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Abstract/Contents

Abstract
Markov chains and random processes are ubiquitous in statistical and scientific applications. The main contribution of this thesis is to introduce the study of Markov chains on double cosets. Given a group and two sub-groups, the double cosets define equivalence classes of the group. A random walk on the group then induces a random processes on the set of equivalence classes. When is this random process also a Markov chain? Surprisingly, some of the examples capture scientifically (and mathematically) interesting special cases. This thesis develops a general theory for double coset Markov chains. Several cases are investigated in detail. Special attention is given to the example in which parabolic subgroups of the permutation group are indexed by contingency tables. In each of the examples the general theory developed here is applied to understand the eigenvalues, eigenfunctions, and the mixing times of the Markov chains. In the second half of this thesis two more new random processes are studied: A Markov chain on a space of coalescent trees (also related to a double coset space) and an urn model with an infinite color space. The long-term behavior of each is analyzed.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English

Creators/Contributors

Author Simper, Mackenzie Alice
Degree supervisor Diaconis, Persi
Thesis advisor Diaconis, Persi
Thesis advisor Chatterjee, Sourav
Thesis advisor Palacios Roman, Julia Adela
Degree committee member Chatterjee, Sourav
Degree committee member Palacios Roman, Julia Adela
Associated with Stanford University, Department of Mathematics

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Mackenzie Alice Simper.
Note Submitted to the Department of Mathematics.
Thesis Thesis Ph.D. Stanford University 2022.
Location https://purl.stanford.edu/gk263qs8388

Access conditions

Copyright
© 2022 by Mackenzie Alice Simper
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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