For a noise-optimal coupling out of a measure with a density relative to a diagonal Gaussian measure, the first n coordinates of the target are almost surely a Borel function of the source.
In the setting of Probability Measures on a Hilbert Space Transported in the Noise Norm: Standing Notation, with the maps of Borel Probability Measures on a Real Hilbert Space with an Orthonormal Basis: Standing Notation §coordinates, which are Borel by Borel Sets of a Hilbert Space with an Orthonormal Basis: Coordinates, Determination by Finite-Dimensional Projections, and Pairs §continuity, let be a variance sequence with diagonal Gaussian measure , and let belong to the set of The Measures Noise-Connected to the Reference Measure §space, so that the ordered pair is noise-connected by The Noise Wasserstein Distance is a Metric on the Measures Noise-Connected to the Reference Measure: Existence of Noise-Optimal Couplings, Comparison with the Quadratic Wasserstein Distance and Lower Semicontinuity §connected. Suppose that has a density with respect to , and let be a noise-optimal coupling of and . Let , write for the identity map of , and let carry the product -algebra . The components and of the map are Borel by Borel Sets of a Hilbert Space with an Orthonormal Basis: Coordinates, Determination by Finite-Dimensional Projections, and Pairs §product-sigma and claim 4 of Borel Measurability and Bounded Integration on a Metric Space. By Pairings into a Product, the Graph of a Measurable Map, and Couplings Concentrated on a Graph §pairing, applied with and in place of and , this map and the map for every Borel are measurable into ; image measures are those of claim 1 of Image Measures, Measures with Densities, and Change of Variables.
1. (The head of the target is a function of the source) There is a Borel map with
2. (Almost sure form) Let be as in claim 1. The set , where and , is the preimage under of the graph of , which belongs to by Pairings into a Product, the Graph of a Measurable Map, and Couplings Concentrated on a Graph §graph-measurable, being separable by Euclidean Space is a Separable Metric Space §separable; so , and .
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