= Bayesian network
{c}
{title2=$p(x)=\prod_jp(x_j\mid x_{\operatorname{pa}(j)})$}
{wiki}
A Bayesian network consists of a <Directed acyclic graph> $G$ whose nodes are <random variables>, together with <conditional distributions> satisfying $p(x)=\prod_jp(x_j\mid x_{\operatorname{pa}_G(j)})$. Its <Directed acyclic graph> encodes <conditional independence> constraints. For unrestricted binary variables it has $\sum_j2^{|\operatorname{pa}_G(j)|}$ free <conditional probabilities>. A <Directed acyclic graph> used this way need not have a causal interpretation.
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