Graphical Models and Bayesian Networks with R

Advanced R users from all professional groups. (For introductory R courses please revisit the course list.) Attendees are assumed to have a working understanding of log-linear models for contingency tables.

• CHF 600.- für Angehörige der UZH/ETH und assoziierter Institute
• CHF 800.- für Alumni der UZH/ETH, Angehörige anderer Universtitäten, Forschungseinrichtungen und Ämter des Bundes oder der Kantone, non-profit Organisationen
• CHF 1200.- für Firmen

Introduce participants to working with Graphical Models (GMs) and Bayesian Networks (BNs) in R. This includes probability propagation in BNs and aspects of learning BNs from data using graphical models.

Topics will include:

• Probability propagation with Bayesian networks (BNs) and their implementation in the gRain package.
• A look under the hood of BNs to understand mechanisms of probability propagation.
• Dependency graphs and conditional independence restrictions.
• Learning BNs from data using graphical log-linear models in the gRim package.

Examples from genetics will be used throughout for illustrative purposes. Moreover, there will be a running example about building a BN for a medical diagnosis from real-world data.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.