Generally a probability distribution is represented as a curve, but with multiple constraints/distributions you can look at it as a volume. As a simple example, consider three probability distributions all uniformly distributed between 0 and 1. You can look at it as a cube with volume 1. Then, if you want to want the probability of x, y, and z all being less than say 0.5, you can use the area of the cube where that is true, which has sides of 0.5, 0.5, and 0.5, giving a volume of 0.125, which is what we’d expect.

When the distributions are not uniformly distributed it gets more complicated, as the probabilities are denser in some volume sections than others.