In this post, I want to talk about conditional dependence and independence between events. This is an important concept in probability theory and a central concept for graphical models.
In my two-part post on Bayesian belief networks, I introduced one of the main categories of graphical models. You can read Part 1 and Part 2 by following these links.
This is actually an informal continuation of the two Bayesian networks posts. Even though I initially wanted to include it at the end of Part 2, I decided it’s an important enough topic that deserves its own space.