This paper presents a new method of constructing random functions whose realizations can be evaluated efficiently. The basic idea is to "blend", both stochastically and linearly, a limited set of independent initial realizations previously generated by any chosen simulation method. The blending stochastic coefficients are determined in such a way that the new random function so generated has the same mean and covariance functions as the random function used for generating the initial realizations.