Graphical Models and Network Science
By the end of the course, the students will be able to: • Understand quasi-reaction dynamics and create ODE and SDE stoichiometric models to describe such process. • Apply local linear approximations and Bayesian methods to infer the kinetic coefficients of the quasi-reaction dynamics. • 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.
This course is an introduction to the statistical modeling of social, biological and economical networks. Emphasis will be on statistical methodology and subject-matter-agnostic models, rather than on the specifics of different application areas.
The course will combine lectures with both theoretical tutorials and implementational practical sessions.
There will be 2 assignments during the semester, each counting for 15% of the final grade. A final exam counts for 70% of the final grade.
- The students will be provided with the lecture notes written by the lecturer.