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Seen as a function of <math>y</math> for given <math>x</math>, <math>P(Y=y|X=x)</math> is a probability mass function and so the sum over all <math>y</math> (or integral if it is a conditional probability density) is 1. Seen as a function of <math>x</math> for given <math>y</math>, it is a [[likelihood function]], so that the sum over all <math>x</math> need not be 1.
Additionally, a marginal of a joint distribution can be expressed as the expectation of the corresponding conditional distribution. For instance, <math> p_X(x) = E_{Y}[p_{X|Y}(X \ |\ Y)] </math>.
==Measure-theoretic formulation==
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