P(B) is the prior or marginal probability of B, and acts as a normalizing constant.

Once we have p, the other three cell probabilities can easily be recovered from the marginal probabilities.

This allows for the notion of a reduced or marginal probability on both Alice and Bob's measurements, and is formalised by the conditions:

The denominator is the marginal probability of the data, averaged over all possible parameter values weighted by their prior distribution.

In fact, the result is only affected by the relative marginal probabilities of winning and ; in particular, the probability of a draw is irrelevant.

Being nth in line: With marginal probability (1/N), under the Principle of indifference.

For marginal probability in probability theory, see "Marginal distribution"

In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities.

Note that a marginal probability can always be written as an expected value:

Similarly, the (marginal) probability of a visible (input) vector of booleans is the sum over all possible hidden layer configurations: