MATH 383 - Statistical Inference for Network Data

Semester Offered: Spring
1 unit(s)

This course introduces statistical tools, theory, and methodology for analyzing complex social systems with network models. Students are introduced to standard network constructions and associated centrality metrics, clustering algorithms, and dynamical models through classic papers and examples. A main focus of the course is generative models for social network data and the theory and computation of associated inference problems, including MCMC sampling. In addition to the Erdos-Renyi and stochastic block models, special attention ispaid to the family of Exponential Random Graph Models and Latent Space models, which have found significant applications across the social sciences in recent years. Students gain experience performing inference and doing analysis on real data using the R programming language. The Department.

Prerequisite(s): AP credit in statistics, MATH 141 or MATH 240; MATH 221, and MATH 241.

Three 50-minute periods.

Course Format: CLS