Hello everyone.


I'm looking for a "simple" explanation of the concept of D-separation in a Bayesian Network.


As far as I know the **definition** is "two variables (nodes) in the network are D-Separated if the information is "blocked" between the two nodes by some evidence about the nodes in the middle.


But I can't pratically understand the concept.


I don't have enough rep to use images so these are the links to the images (just 25kbyte each one) from my dropbox folder.


Pattern 1: <https://dl.dropbox.com/u/60525806/Run_NoEvidence%28Sum%20Propagate%20Normal%29.JPG>
Pattern 1: <https://dl.dropbox.com/u/60525806/Run_noEvidence%28Sum%20Propagate%20Normal%29%20_2.JPG>
Pattern 3:
<https://dl.dropbox.com/u/60525806/Run_NoEvidence%28Sum%20Propagate%20Normal%29%20_3.JPG>


What I would like to understand if in these three patterns:


1. When there is **NO evidence** What (and why) nodes are D-separated from the each others
2. In what cases **EVIDENCE** about a specific node cause two nodes to be separated


Thank you in advance for any help.


Regards 



A "simple" explanation of the concept of D-separation in a Bayesian Network?
Try this tutorial <http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html>


