Computational methods and mathematical measures for population relationships
Abstract/Contents
- Abstract
- A population is the most central unit of study in population genetics. Patterns revealed by data on variation, both genetic and cultural, are important in understanding the composition and dynamics of populations. These patterns help unravel the driving forces behind evolutionary processes. Numerous statistics, mathematical models, and computational approaches have been developed to quantitatively analyze relationships within and between populations. To build upon these efforts, this dissertation investigates and devises computational methods and mathematical measures for studying various aspects of populations. Chapters 2, 3, and 4 investigate the mathematical properties of genetic diversity measures used to quantify the variation within and between populations. Chapters 5 and 6 study the challenge of cluster alignment in analyzing and visualizing population structure analysis results. Chapters 7 and 8 adapt population-genetic approaches to study the patterns of composition and change in cultural traits. Collectively, the studies conducted in the dissertation introduce tools to improve existing approaches, to devise new frameworks, and to resolve previously unaddressed issues in analyzing population relationships. The advancements also demonstrate potential applications in fields beyond population genetics.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Liu, Xiran |
|---|---|
| Degree supervisor | Rosenberg, Noah |
| Thesis advisor | Rosenberg, Noah |
| Thesis advisor | Feldman, Marcus |
| Thesis advisor | Palacios, Julia |
| Degree committee member | Feldman, Marcus |
| Degree committee member | Palacios, Julia |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xiran Liu. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/xr628fd6019 |
Access conditions
- Copyright
- © 2023 by Xiran Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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