Given an undirected graph $G$, pairwise Markov statements are statements of the form \{$v$, $w$, all other vertices\}\ for each pair of non-adjacent vertices $v$ and $w$ of $G$.
For example, for the undirected 5-cycle graph $G$, that is, the graph on $5$ vertices with edges $a—b—c—d—e—a$, we get the following pairwise Markov statements:
|
|
Given a directed acyclic graph $G$, pairwise Markov statements are statements of the form \{$v$, $w$, nondescendents($G,v$)-$w$\}\ for each vertex $v$ of $G$ and each non-descendent vertex $w$ of $v$. In other words, for every vertex $v$ of $G$ and each nondescendent $w$ of $v$, this method returns the statement: $v$ is independent of $w$ given all other nondescendents.
For example, given the digraph $D$ on $7$ vertices with edges $1 \to 2, 1 \to 3, 2 \to 4, 2 \to 5, 3 \to 5, 3 \to 6, 4 \to 7, 5 \to 7$, and $6\to 7$, we get the following pairwise Markov statements:
|
|
This method displays only non-redundant statements. In general, given a set $S$ of conditional independent statements and a statement $s$, then we say that $s$ is a a redundant statement if $s$ can be obtained from the statements in $S$ using the semigraphoid axioms of conditional independence: symmetry, decomposition, weak union, and contraction as described in Section 1.1 of Judea Pearl, Causality: models, reasoning, and inference, Cambridge University Press. We do not use the intersection axiom since it is only valid for strictly positive probability distributions.
The object pairMarkov is a method function.