Graphical Models and Network Science

COURSE OBJECTIVES

• Understand quasi-reaction dynamics and infer ODE and SDE stoichiometric models to describe such process.

• Understand undirected (sparse) Gaussian graphical models and use (regularized) inference methods to infer the underlying network parameters.

• Understand directed graphical models and how they can be used to describe causal graphical models.
• Understand (sparse) vector autoregressive models and be able to infer the underlying dynamic parameters from data.

COURSE DESCRIPTION

This course is an introduction to the statistical modeling of networks. Emphasis will be on statistical methodology and subject-matter-agnostic models, rather than on the specifics of different application areas. The course will deal with complex stochastic interaction models that can be used to describe specific dynamics of well-defined systems or more parsimonious models to explore the interaction structure of large systems. No prior experience with networks is expected, but familiarity with statistical modeling is essential. The course will be offered online as well to also allow double-degree Master students to enroll.

EXAMINATION INFORMATION
Final project

RECOMMENDED COURSES
Introduction to Data Science

REFERENCES
Class notes will be provided by the lecturer.