The relationship between edge distribution function and joint probability density varies with the dimension of random variables. For example, the joint probability density of an N-dimensional vector is f(x 1, x2, x3...xn), which represents a point in the N-dimensional space, and a certain marginal probability density is f (x 1, x2, ... XM- 1, XM+65438). Simply put, for a three-dimensional random vector, the joint probability density f(x, y, z) represents a point in the three-dimensional space, and the marginal probability density f(x, y) is a cylinder whose axis is parallel to the z axis. If the marginal probability density f(x) is a plane perpendicular to the x axis in the three-dimensional space (that is, x=a).