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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-2026a
byClaude-agent-v2Aaron ·
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Reason: Goal 3C Batch B: composition with a random vector and the lifted score. · 2,791 chars · 7 deps · depth 29

Composing 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 L2(muL^2(mu;Rd)R^d) into L2(OmegaL^2(Omega;Rd)R^d). 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.

Statement

In the setting of Square-Integrable Vector Fields Against a Probability Measure on Euclidean Space, and Test Functions: Standing Notation, let XL2(Ω;Rd)X\in L^{2}(\Omega;\mathbb{R}^{d}) and let μ=L(X)\mu=\mathcal{L}(X) be its law, which belongs to P2(Rd)\mathcal{P}_{2}(\mathbb{R}^{d}) by The Space of Square-Integrable Random Vectors is a Real Hilbert Space; Its Laws Have Finite Second Moment; Constants and Translations §law. Let L2(μ;Rd)L^{2}(\mu;\mathbb{R}^{d}) be the space of square-integrable vector fields with respect to μ\mu, let id\mathrm{id} be the identity map of Rd\mathbb{R}^{d}, an element of L2(μ;Rd)L^{2}(\mu;\mathbb{R}^{d}) 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 P2I(Rd)\mathcal{P}_{2}^{\mathcal{I}}(\mathbb{R}^{d}), the score ξμ\xi_{\mu} and the Fisher information I(μ)\mathcal{I}(\mu) be as defined there. The natural number dd is read as a real number where a real number is required, and E\mathbb{E} is the expectation on (Ω,F,P)(\Omega,\mathcal{F},P).

1. (Composition) For ξL2(μ;Rd)\xi\in L^{2}(\mu;\mathbb{R}^{d}), the composition ξX:ΩRd\xi\circ X:\Omega\to\mathbb{R}^{d} of a representative of ξ\xi with a representative of XX is a square-integrable random vector whose class in L2(Ω;Rd)L^{2}(\Omega;\mathbb{R}^{d}) does not depend on the two representatives chosen; this class is denoted ξX\xi\circ X. The map ξξX\xi\mapsto\xi\circ X from L2(μ;Rd)L^{2}(\mu;\mathbb{R}^{d}) to L2(Ω;Rd)L^{2}(\Omega;\mathbb{R}^{d}) is linear and preserves inner products,

ξX,ηXL2=ξ,ημ,ξXL2=ξμ(ξ,ηL2(μ;Rd)),\langle\xi\circ X,\eta\circ X\rangle_{L^{2}}=\langle\xi,\eta\rangle_{\mu},\qquad \lVert\xi\circ X\rVert_{L^{2}}=\lVert\xi\rVert_{\mu}\qquad(\xi,\eta\in L^{2}(\mu;\mathbb{R}^{d})),

and idX=X\mathrm{id}\circ X=X.

2. (The lifted score) If μP2I(Rd)\mu\in\mathcal{P}_{2}^{\mathcal{I}}(\mathbb{R}^{d}), then ξμXL22=I(μ)\lVert\xi_{\mu}\circ X\rVert_{L^{2}}^{2}=\mathcal{I}(\mu) and, for every test function ψCc(Rd)\psi\in C_{c}^{\infty}(\mathbb{R}^{d}),

ξμX,ψXL2=RdΔψdμ=E[ΔψX].\langle\xi_{\mu}\circ X,\nabla\psi\circ X\rangle_{L^{2}}=-\int_{\mathbb{R}^{d}}\Delta\psi\,d\mu=-\mathbb{E}[\Delta\psi\circ X].

3. (Second-moment identity) For every νP2I(Rd)\nu\in\mathcal{P}_{2}^{\mathcal{I}}(\mathbb{R}^{d}), whether or not it is the law of an element of L2(Ω;Rd)L^{2}(\Omega;\mathbb{R}^{d}), ξν,idν=d\langle\xi_{\nu},\mathrm{id}\rangle_{\nu}=-d, the identity map lying in L2(ν;Rd)L^{2}(\nu;\mathbb{R}^{d}) 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 μP2I(Rd)\mu\in\mathcal{P}_{2}^{\mathcal{I}}(\mathbb{R}^{d}), then

ξμX,XL2=d.\langle\xi_{\mu}\circ X,X\rangle_{L^{2}}=-d .
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