What is D-Separation? | Conditional Independence

D-Separation describes conditional independence in Directed Graphical Models. We can use this in order to determine relationships between random variables if only a subset of the model is observable. Here are the notes: raw.githubuser...
The crucial point in conditional independence is the location of observable nodes in the Directed Graphical Model. Based on triplets, we can define three simple rules that allow us to check for conditional independence in arbitrarily structured graphs.
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Timestamps:
00:00 Opening
00:23 Introduction
03:42 1st Example
07:21 2nd Example
09:27 3rd Example
11:48 Three basic rules
14:45 Algorithm with Example
17:31 Summary