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	\myheading{Module 19.9: Bayesian Networks: Formal Semantics}
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                We are now ready to formally define the semantics of a Bayesian Network
                \begin{block}{Bayesian Network Semantics:}
			A Bayesian Network structure G is a directed acyclic graph where nodes represent random variables $X_1,X_2,...,X_n$. Let $P_{a_{X_i}}^G$ denote the parents of $X_i$ in G and NonDescendants($X_i$) denote the variables in the graph that are not descendants of $X_i$. Then G encodes the following set of conditional independence assumptions called the local independencies and denoted by $I_I(G)$ for each variable $X_i$. ($X_i \perp$ NonDescendants($X_i$)$|P_{a_{X_i}}^G$)
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				\item We will see some more formal definitions and then return to the question of independencies.
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