We can also express the strength of the relationship between nodes as Mutual Information percentage (MI%), the percentage reduction in entropy of node Y given information about X. Zero MI implies conditional independence – node X does not give any information about the state of node Y. MI (and MI%) can be computed for any pair of nodes, regardless of whether they are directly connected.
Unlike S, a value zero in any cell of S−1 implies conditional independence among those variables.
All d-separation relations between nodes in a graph imply conditional independence relations between the corresponding variables.
The ability of the tumor to generate its own vasculature in the ectopic environment implied a conditional existence of CSCs, i.e. evident in one in vivo environment but not in the other (and not in vitro).
Thus, we classically employ the conditional independence assumption [ 5], which implies that conditional to X, there are no more variables related to Y, in other words, given any set of variables Z, we have f(Y, Z| X)= f(Y| X)· f(Z| X).
The ordering implies a conditional: if, and only if, you love food, will you then love life, in the right way.
As its name implies, this conditional probability function is a maximum-entropy distribution, constrained to conform with the binary feature frequencies observed in the training data.
The exclusion restriction implies that, conditional on controls, the mortality rates of European settlers have no effect on the size distribution today.
For this method to work, a manifold assumption is made that, when mapping the instances, if two points which have similar intrinsic geometry then it implies their conditional distributions are similar.
