Hilbert function space splittings on domains with infinitely many variables

We present an approach to defining Hilbert spaces of functions depending on infinitely many variables or parameters, with emphasis on a weighted tensor product construction based on stable space splittings, The construction has been used in an exemplary way for guiding dimension- and scale-adaptive algorithms in application areas such as statistical learning theory, reduced order modeling, and information-based complexity. We prove results on compact embeddings, norm equivalences, and the estimation of $epsilon$-dimensions. A new condition for the equivalence of weighted ANOVA and anchored norms is also given.

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