Graphical Models and Network Science
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.
- Understand quasi-reaction dynamics and infer ODE and SDE stoichiometric models to describe such process.
- Describe relational event processes as an SDE and use ideas from survival analysis to infer the models from relational data.
- 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.
Lectures and tutorials
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
- Master of Science in Financial Technology and Computing, Lecture, Elective, 2nd year