Overview
This course aims to teach students how to model and replicate persistence diagrams, which are useful displays summarizing topological features. The course covers topics such as the Replicating Statistical Topology (RST) approach, parametric modeling, closeness relations between points, and considering diagram shapes. The teaching method includes introducing concepts, presenting examples, and comparing different models through examples. This course is intended for individuals interested in statistical inference, topology, and data analysis.
Syllabus
Introduction
Example
Bootstrap
Gibbs distribution
Treasure of Delta
Pseudomaximum likelihood estimation
MCMC
Replicating persistent diagrams
Proposal distribution
Comparison
Vertical clustering
Results
Topological signals
Backplot
Taught by
Applied Algebraic Topology Network