Probabilistic Graphical Model (PGM, or Graphical Model) is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov random fields.
Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph.
Bayesian Network Syntax
The graph: G = {V, E}
A set of local conditional distributions: P = {
}
Bayesian Network Semantics
A Bayesian Network N with nodes defined a joint distribution
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