This structure shows which two variables are conditionally independent.

A node is conditionally independent of the entire network, given its Markov blanket.

Because of the Markov assumption, the probability of the current true state given the immediately previous one is conditionally independent of the other earlier states.

Because of the Markov assumption, the true state is conditionally independent of all earlier states given the immediately previous state.

Any two nodes are conditionally independent given the values of their parents.

In general, any two sets of nodes are conditionally independent given a third set if a criterion called d-separation holds in the graph.

Suppose these Xs are conditionally independent given p.

The observed items are conditionally independent of each other given an individual score on the latent variable(s).

The method asserts that the Sudoku row and column constraints are, to first approximation, conditionally independent given the box constraint.

An important assumption made in this model is that and are conditionally independent given .