Inference for Stochastic Processes: Large Networks Analysis

This lecture by the instructor covers the inference for stochastic processes, focusing on large networks analysis. Topics include understanding large networks through adjacency matrices, limiting behavior in large networks, intrinsic scales of large networks, analysis of social media, and the analysis of different regions of the brain. The lecture also delves into the general setup of statistical models for network data, multivariate network data, and the need for new theories and methods in equatorial drifters. The presentation concludes with a discussion on spatial processing, data density, and the importance of new theories and methods in large network analysis.