INF2D: Week 9: Inference over Time

Bayesian Networks model static environments  (ie., while each random variable has a range of possible values, its actual value doesn't change over time).  If we want to model uncertainty in dynamic environments as well (ie, do inference when the value of each random variable can change over time) we will need additional tools.

This week we will cover:

25: Time and Uncertainty I

26: Time and Uncertainty II

27: Dynamic Bayesian Networks

As always, these lectures will be delivered in person and live streamed.  If there is anything you don't understand, then you have several options: 

1. Post a question on piazza;
2. Ask a question at the in-person lecture; and/or
3.  Ask your tutor.