A number of approaches for discretizing partial differential equations with random data
are based on generalized polynomial chaos expansions of random variables. These constitute
generalizations of the polynomial chaos expansions introduced by Norbert Wiener to
expansions in polynomials orthogonal with respect to non-Gaussian probability measures. We
present conditions on such measures which imply mean-square convergence of generalized
polynomial chaos expansions to the correct limit and complement these with illustrative
examples.