 However, in many applications the starting point is really the finite-dimensional distributions of the stochastic process.The theorem says that provided the finite-dimensional distributions satisfy the obvious consistency requirements, one can always identify a probability space to match the purpose.In many situations, this means that one does not have to be explicit about what the probability space is.