Consider the following characterization of the Bayes' theorem:

Bayes' TheoremGiven some observed data $x$, the posterior probability that the paramater $\Theta$ has the value $\theta$ is $p(\theta \mid x) = p(x \mid \theta) p (\theta) / p(x)$, where $p(x \mid \theta)$ is the likelihood, $p(\theta)$ is the prior probability of the value $\theta$, and $p(x)$ is

the marginal probabilityof the value $x$.

Is there any special reason why we call $p(x)$ the "marginal probability"? What is "marginal" about it?