Events
Department of Mathematics and Statistics
Texas Tech University
Persistent homology is a tool in topological data analysis for learning about the geometrical/topological structures in data by detecting different dimensional holes and summarizing their appearance disappearance scales in persistence diagrams. However, quantifying the uncertainty present in these summaries is challenging. In this talk, I will present a Bayesian framework for persistent homology by relying on the theory of point processes. This Bayesian model provides an effective, flexible, and noise-resilient scheme to analyze and classify complex datasets. A closed form of the posterior distribution of persistence diagrams based on a family of conjugate priors will be provided. The goal is to introduce a supervised machine learning algorithm using Bayes factors on the space of persistence diagrams. This framework is applicable to a wide variety of datasets. I will present an application to a materials science problem.
The Statistics seminar may be attended online at 4:00 PM CST (UTC-6) via this Zoom link.
Meeting ID: 915 4575 0834
Passcode: 552577
See PDF abstract.
Watch online via this Zoom link.
We will give an overview of the Number Field Sieve, which is the fastest known algorithm for factoring integers on a digital computer.
 | Wednesday
Nov. 12
4 PM
Math 011
|
| Applied Mathematics and Machine Learning
TBA
Laurent Jay
Department of Mathematics, The University of Iowa
|
Abstract. TBA.
When: 4:00 pm (Lubbock's local time is GMT -5)
Where: room Math 011 (Math Basement)
ZOOM details:
- Choice #1: use this link
Direct Link that embeds meeting and ID and passcode.
- Choice #2: join meeting using this link
Join Meeting, then you will have to input the ID and Passcode by hand:
* Meeting ID: 915 2866 2672
* Passcode: applied
 | Thursday
Nov. 13
6:30 PM
MA 108
|
| Mathematics Education
Math Circle
Erhan Guler
Mathematics and Statistics, Texas Tech University
|
Math Circle Fall Flyer
abstract noon CST (UTC-6)
Zoom link available from Dr. Brent Lindquist upon request.