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Building Probabilistic Graphical Models with Python by Kiran R Karkera

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D-separation

Having understood that the direction of arrows indicate that one node can influence another node in a Bayes network, let's see how exactly influence flows in a Bayes network. We can see that the grades eventually influence the job offer, but in the case of a very big Bayes network, it would not help to state that the leaf node is influenced by all the nodes at the top of the Bayes network. Are there conditions where influence does not flow? We shall see that there are simple rules that explain the flow of influence in the following table:

No variables observed

Y has been observed

D-separation

The preceding table depicts ...

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