As seen in chaos expansions kind of, and used in low rank kriging, and assumed in PCA and implied by many basis decompositions etc.
Suppose we have a collection of real-valued functions on our index space , and a collection of uncorrelated random variables. Now we define the random process We might care about the first two moments of , i.e. and variance function
Now suppose that we have a stochastic process where the index is a compact domain in . The corresponding expansion of in the above form is known as the Karhunen-Loève expansion. Suppose that has covariance function and define an operator , taking the space of square integrable functions on to itself, by Suppose that , and , are, respectively, the (ordered) eigenvalues and normalised eigenfunctions of the operator. That is, the and solve the integral equation with the normalisation These eigenfunctions lead to a natural expansion of , known as Mercer’s Theorem, which states that where the series converges absolutely and uniformly on . The Karhunen-Loève expansion of is obtained by setting , so that Now, when does such an expansion exist?
Because I look at signals on unbounded domains a lot, the case where a countable basis cannot be assumed to exist seems important.
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