Statistics for Topological and Discrete Data

Topological and discrete data such as trees and networks constitute strongly non-Euclidean structures, often beyond the reach of traditional statistical tools. Their analysis is often challenged by computational tractability, as natural constraints such as permutation invariance are computationally hard to implement. Still, such data can be often transposed into more familiar contexts, whether by transformation, asymptotic arguments, or replicated observations. This workshop will explore both modelling and processing of discrete structures, also drawing in areas such as signal processing on graphs and nonparametrics.

Topics: Part of the Semester : Functional Data Analysis