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Given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X (written "Y | X") is the probability distribution of Y when X is known to be a particular value.
For discrete random variables, the conditional probability mass function can be written as P(Y = y | X = x). From the definition of conditional probability, this is
Similarly for continuous random variables, the conditional probability density function can be written as pY|X(y | x) and this is
where pX,Y(x, y) gives the joint distribution of X and Y, while pX(x) gives the marginal distribution for X.
The concept of the conditional distribution of a continuous random variable is not as intuitive as it might seem: Borel's paradox shows that conditional probability density functions need not be invariant under coordinate transformations.
If for discrete random variables P(Y = y | X = x) = P(Y = y) for all x and y, or for continuous random variables pY|X(y | x) = pY(y) for all x and y, then Y is said to be independent of X (and this implies that X is also independent of Y).
Seen as a function of y for given x, P(Y = y | X = x) is a probability and so the sum over all y (or integral if it is a conditional probability density) is 1. Seen as a function of x for given y, it is a likelihood function, so that the sum over all x need not be 1.
See also
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