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.