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	\myheading{Module 19.6: Independencies encoded by a Bayesian network (Case 1: Node and its parents)}
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			\begin{itemize}
				\onslide<1->{\item First, let us see some independencies which clearly do not exist in the graph.}
				\onslide<2->{\item Is $L \perp G$ (No, by construction)}
				\onslide<3->{\item Is $G \perp D$ (No, by construction)}
				\onslide<4->{\item Is $G \perp I$ (No, by construction)}
				\onslide<5->{\item Is $S \perp I$ (No, by construction)}
				\onslide<6->{\item \textbf{Rule?} A node is not independent of its parents.}
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		     	\onslide<1->{\item Rule : A node is not independent of its parents even whwn we are given other variables}
		     	\onslide<2->{\item NO, the instructor is not going to look at the SAT score but the grade}
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				\onslide<1->{\item Let us focus on $G$ and $L$.}
				\onslide<2->{\item We already know that $G \not\perp L$.}
				\onslide<3->{\item What if we know $l = 1|0$? Does $G$ become independent of $L$?}
				\onslide<4->{\item No (intuitively, the student may be intelligent or not but ultimately, the record depends on the performance in the course.)}
				\onslide<5->{\item If we know that $D=1|0$, does $G$ become independent of $L$.}
				\onslide<6->{\item No (intuitively, the course may be easy or hard but the letter would depend on the performance in the course)}
				\onslide<7->{\item What if we know the value of $S$? Does $G$ become independent of $L$?}
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			\textbf{The same argument can be made about the following pairs}
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				\onslide<1->{\item $G\not\perp D$ (even when other variables are known).}
				\onslide<2->{\item $G \not\perp I$ (even when other variables are known).}
				\onslide<3->{\item $S \not\perp l$ (even when other variables are known).}
				\onslide<4->{\item \textbf{Rule:} A node is not independent of its parents even when we are aware of the other variables.}
				\onslide<5->{\item No, the instructor is not going to look at the SAT Score, but on the grade.}
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