Short Course on Graphical Models
This course aims at introducing probabilistic graphical models, which provide a unified framework for learning dependence relationships between random variables and making statistical inference under complex multivariate settings. Participants will learn the fundamentals of graphical models, including Bayesian Networks and Markov Random Fields, and explore applications in machine learning, data analysis, and decision-making.
Teaching Methodology:
- Theoretical notions and statistical methodologies will be introduced throughout the lectures
- Participants will engage in practical exercises using popular graphical modeling tools
- Real-world applications and case studies will be explored to connect theory with practice
Additional Resources
Textbooks and Readings:
- Introduction to graphical modelling (Edwards, D.)
- Handbook of Graphical Models (Maathuis, M. et al.)
Software Textbooks:
- Graphical Models with R (Hojsgaard, S. et al.)
- Bayesian Networks: With examples in R (Scutari, M. and Denis, J.B.)
- BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs (Castelletti, F. and Mascaro, A.)
Prerequisites
- Basic understanding of probability theory and familiarity with concepts in linear algebra.
- Consolidated knowledge of the R software is also required.
Info:
Prof. Alberto Roverato - Department of Statistical Sciences, University of Padua
e-mail: alberto.roverato@unipd.it
