This is a 12-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.


Meeting times:

The course will take place through on-line meetings (Zoom).

1:00pm-3:10pm Eastern Time (10am-12:10pm Pacific Time)

Thursday, November 5, 2020

Friday, November 6, 2020

Monday, November 9, 2020

Tuesday, November 10, 2020

Thursday, November 12, 2020

Friday, November 13, 2020



Elementary college-level math and computer skills, basic data processing skills through tools such as Excel.  No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks.  We will cover all that is required in the course.  While all concepts covered in the course are general, we will use GeNIe to illustrate them.  Tuition covers a 30-day GeNIe license for use during the course.


Tuition fee:

Course tuition fee $500 ($300 for students)

There is a minimum of 5 and a maximum of 20 participants.


For more information/to register: