Short Course on Graphical Models

Department of Statistical Sciences, University of Padua - Via Cesare Battisti 241, PADUA

Dal 02.09.2024 al 03.09.2024

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:

Software Textbooks:


  • Basic understanding of probability theory and familiarity with concepts in linear algebra.
  • Consolidated knowledge of the R software is also required.

Prof. Alberto Roverato - Department of Statistical Sciences, University of Padua


Pdf file