Generalized Stochastic Processes: Linear Relations to White Noise and Orthogonal Representations

Abstract

We present two linear relations between an arbitrary (real tempered second order) generalized stochastic process over ${\mathbb{R}}^d$ and White Noise processes over ${\mathbb{R}}^d$. The first is that any generalized stochastic process can be obtained as a linear transformation of a White Noise. The second indicates that, under dimensional compatibility conditions, a generalized stochastic process can be linearly transformed into a White Noise. The arguments rely on the regularity theorem for tempered distributions, which is used to obtain a mean-square continuous stochastic process which is then expressed in a Karhunen-Loève expansion with respect to a convenient Hilbert space. The first linear relation obtained allows also to conclude that any generalized stochastic process has an orthogonal representation as a series expansion of deterministic tempered distributions weighted by uncorrelated random variables with summable variances. This representation is then used to conclude the second linear relation.