Composition of a Square-Integrable Vector Field with a Random Vector, and the Lifted Score: Isometry, Norm, Weak Identity and Second-Moment Identity
lemmaAnalysisProbabilitylem:score-lift-2026aComposing a square-integrable vector field with a random vector of law mu gives a well-defined square-integrable random vector, and this composition is a linear isometry from ; into ;. For a law with finite Fisher information the lifted score has squared norm equal to the Fisher information, pairs with lifted gradients of test functions to minus the expectation of the Laplacian, and pairs with X itself to minus the dimension.
In the setting of Square-Integrable Vector Fields Against a Probability Measure on Euclidean Space, and Test Functions: Standing Notation, let and let be its law, which belongs to by The Space of Square-Integrable Random Vectors is a Real Hilbert Space; Its Laws Have Finite Second Moment; Constants and Translations §law. Let be the space of square-integrable vector fields with respect to , let be the identity map of , an element of by Basic Properties of the Tangent Space: Closed Subspace, the Identity Map Belongs to It, Second-Moment Limits, and Representation of Bounded Functionals on Gradients §identity, and let finite Fisher information, the set , the score and the Fisher information be as defined there. The natural number is read as a real number where a real number is required, and is the expectation on .
1. (Composition)¶ For , the composition of a representative of with a representative of is a square-integrable random vector whose class in does not depend on the two representatives chosen; this class is denoted . The map from to is linear and preserves inner products,
and .
2. (The lifted score)¶ If , then and, for every test function ,
3. (Second-moment identity)¶ For every , whether or not it is the law of an element of , , the identity map lying in by Basic Properties of the Tangent Space: Closed Subspace, the Identity Map Belongs to It, Second-Moment Limits, and Representation of Bounded Functionals on Gradients §identity. In particular, if , then
Loading…
Prerequisites
No prerequisites tracked.
Dependents
No dependents yet.
Dependent proofs
No dependent proofs yet.
No relations recorded yet.