InstructorDr. Marco Scutari, Senior Researcher in Bayesian Networks and Graphical Models, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Polo Universitario Lugano, Switzerland

Course Objective

This course will introduce attendees to recent developments of Bayesian Networks using the statistical language R.

Li Yijie , ASSEE 2017

The whole program reaches an excellent balance!

I learnt a lot about duration analysis, including both theories and how to do empirical analysis with it. The lab sessions are very helpful in improving my understandings of the contents of the lectures. The course is great, and the dinners and excursions are also great.

Description

Day 1: Definitions and Fundamentals
Day 2: Models and Distributional Assumptions
Day 3: Probabilistic Inference
Day 4: Structure Learning
Day 5: Causal Inference

Reading List:

“Bayesian Networks with Examples in R,” Routhledge, (2022) link
“Causal Inference in Statistics: A Prime”, Wiley & Sons Ltd, (2016) link