A natural way to represent a 1-D probability distribution is to store its cumulative distribution function (cdf) F(x) = Prob(X≤ x). When several random variables X_1,..., X_n are independent, the corresponding cdfs F_1(x_1),..., F_n(x_n) provide a complete description of then-joint distribution. In practice, there is usually some dependence between the variables, so, in addition to the marginals F_i(x_i), we also need to provide an additional information about the joint distribution of the given variables. It is possible to represent this joint distribution by a multi-D cdf F(x_i, ...,x_n) = Prob(X_1 ≤ 展开▼
