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:
- When there is NO evidence What (and why) nodes are D-separated from the each others
- In what cases EVIDENCE about a specific node cause two nodes to be separated
Thank you in advance for any help.