Graduate Seminar

Location: ED 438 and Live Stream

Speaker: Baruch Sokaribo

Title: Analyzing Distributions, Using a Systematic Programmable Approach as Persistent Homology

Abstract:

Persistent homology is a tool in mathematics used for analyzing data topologically. This analysis is made possible through one of its components called the filtered simplicial complex, which is a sequence of nested simplicial complexes. With this, persistent homology can measure the number of connected components (clusters) and get its persistent ${\pi }_0$ (gaps).

This project focuses on the comparison of three fundamental probability distributions which are the Normal distribution, Uniform distribution, and Exponential distribution; using a Python code to derive persistent ${\pi }_0$ and to form a filtration from the analysis. One of the main essences of this comparison is to enable us to categorize any random data into a distinct distribution after analyzing its behavior with the Python code. This project provides worthwhile insights into the behavior and characteristics of the three fundamental distributions through the spectacles of topology.

Live Stream:

https://uregina-ca.zoom.us/j/92053635111?pwd=U25NcEp6amlGV1YrRDdnUm5qNlhaUT09