Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.12202/8951| Title: | Mathematical stochastic models for DNA |
| Authors: | Nandori, Peter Moise, Yonah |
| Keywords: | molecular evolution genetic data algorithmic style Python Monte Carlo probabilities |
| Issue Date: | May-2023 |
| Publisher: | Yeshiva University |
| Citation: | Moise, Y. (2023, May). Mathematical stochastic models for DNA [undergraduate honors thesis, Yeshiva University]. |
| Series/Report no.: | Jay and Jeanie Schottenstein Honors Program;May 2023 |
| Abstract: | Kimura’s neutral theory of molecular evolution gave rise to several models by which to study genetic data. Such models include the infinite alleles model and the infinite sites model. We studied these models, and present them here in an clear and algorithmic style. We coded these models in Python, using Monte Carlo methods to calculate probabilities and perform tests of hypothesis against real-world data. |
| Description: | Undergraduate honors thesis / Open Access |
| URI: | https://hdl.handle.net/20.500.12202/8951 |
| Appears in Collections: | Jay and Jeanie Schottenstein Honors Student Theses |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Yonah Moise Honors Thesis May 2023 OA.pdf | 542.8 kB | Adobe PDF |  View/Open |
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