In this work we analyze the dimension-independent convergence property of an
abstract sparse quadrature scheme for numerical integration of functions of
high-dimensional parameters with Gaussian measure. Under certain assumptions of
the exactness and the boundedness of univariate quadrature rules as well as the
regularity of the parametric functions with respect to the parameters, we
obtain the convergence rate O(N−s), where N is the number of indices,
and s is independent of the number of the parameter dimensions. Moreover, we
propose both an a-priori and an a-posteriori schemes for the construction of a
practical sparse quadrature rule and perform numerical experiments to
demonstrate their dimension-independent convergence rates
