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Cameron–Martin theorem: (Th)∗μ≪μ  ⟺  h∈Hμ(T_h)_*\mu \ll \mu \iff h \in H_\mu(Th​)∗​μ≪μ⟺h∈Hμ​

Proved
HairerSPDE.cameron_martin

by Lucas · Sep 12, 2026 · Mathlib 0df444a (Lean v4.33.1)

functional-analysisgaussian-measuresprobabilityspde

Theorem 4.44 (Cameron–Martin). Let BBB be a separable Banach space and let μ\muμ be a centred Gaussian measure on BBB: a Borel probability measure such that the image of μ\muμ under every continuous linear functional ℓ∈B∗\ell \in B^{*}ℓ∈B∗ is a real Gaussian law, with ∫Bx μ(dx)=0\int_B x\,\mu(dx)=0∫B​xμ(dx)=0. For h∈Bh \in Bh∈B let Th:B→BT_h : B \to BTh​:B→B be the translation Th(x)=x+hT_h(x) = x+hTh​(x)=x+h, and let ∥h∥μ\|h\|_\mu∥h∥μ​ and Hμ={h:∥h∥μ<∞}H_\mu = \{h : \|h\|_\mu < \infty\}Hμ​={h:∥h∥μ​<∞} be the Cameron–Martin norm and space of μ\muμ. Then

(Th)∗μ ≪ μ⟺h∈Hμ.(T_h)_{*}\mu \ \ll\ \mu \qquad \Longleftrightarrow \qquad h \in H_\mu .(Th​)∗​μ ≪ μ⟺h∈Hμ​.

That is, the translated measure is absolutely continuous with respect to μ\muμ precisely for the directions of finite Cameron–Martin norm; for every other direction the two measures are not merely inequivalent but mutually singular.

This is the structural theorem of infinite-dimensional Gaussian analysis. In finite dimensions every direction is admissible, because Lebesgue measure is translation invariant; the theorem says that in infinite dimensions the admissible directions form the proper subspace HμH_\muHμ​, which typically carries measure zero. It is the source of the Girsanov transform for Gaussian noise, of the rate function in Schilder-type large deviation principles, and of the domain of the Malliavin derivative.

Formalization Note. Gaussianity is the hypothesis that the push-forward under each continuous linear functional is a real Gaussian; centredness is the separate hypothesis ∫Bx μ(dx)=0\int_B x\,\mu(dx)=0∫B​xμ(dx)=0. The translation is x↦x+hx\mapsto x+hx↦x+h and absolute continuity is the usual "null sets of μ\muμ are null sets of (Th)∗μ(T_h)_*\mu(Th​)∗​μ". The space BBB is assumed complete and second countable, which is the separable Banach hypothesis of the source.

Preamble
import Mathlib
import Definitions.Def_HairerSPDE_CameronMartin

set_option autoImplicit false

open MeasureTheory ProbabilityTheory Filter
open scoped ENNReal NNReal Topology
Formal statement
namespace HairerSPDE

theorem cameron_martin {B : Type*} [NormedAddCommGroup B] [NormedSpace ℝ B] [MeasurableSpace B]
    [BorelSpace B] [CompleteSpace B] [SecondCountableTopology B]
    (μ : Measure B) [IsGaussian μ] (hμ : μ[id] = 0) (h : B) :
    μ.map (fun x ↦ x + h) ≪ μ ↔ cameronMartinNorm μ h ≠ ∞ := by sorry

end HairerSPDE
Source
M. Hairer, *An Introduction to Stochastic PDEs*, lecture notes, arXiv:0907.4178v2 (3 Jul 2023), p. 31, Theorem 4.44 (Cameron–Martin)
Read-back

What the Lean code literally says, in plain math · Aristotle (Harmonic)

Provenance note: this read-back was written by the agent that drafted the Lean statements, not by an independent auditor, and should be weighed accordingly.

Throughout, BBB is a real normed vector space that is complete and second countable (a separable Banach space), equipped with its Borel σ\sigmaσ-algebra, and B∗B^{*}B∗ denotes its continuous dual. For a measure μ\muμ on BBB:

  • "μ\muμ is Gaussian" means: for every continuous linear functional ℓ:B→R\ell : B \to \mathbb Rℓ:B→R, the push-forward of μ\muμ under ℓ\ellℓ is the real Gaussian law with mean ∫Bℓ dμ\int_B \ell \, d\mu∫B​ℓdμ and variance max⁡(Var⁡[ℓ;μ],0)\max(\operatorname{Var}[\ell;\mu], 0)max(Var[ℓ;μ],0); the variance-zero case is a Dirac mass, and this property already forces μ\muμ to be a probability measure.
  • Cμ(ℓ1,ℓ2)C_\mu(\ell_1,\ell_2)Cμ​(ℓ1​,ℓ2​) denotes the covariance form: it equals ∫B(ℓ1(x)−∫ℓ1dμ)(ℓ2(x)−∫ℓ2dμ) μ(dx)\int_B (\ell_1(x) - \int \ell_1 d\mu)(\ell_2(x) - \int \ell_2 d\mu)\, \mu(dx)∫B​(ℓ1​(x)−∫ℓ1​dμ)(ℓ2​(x)−∫ℓ2​dμ)μ(dx) when the identity map on BBB is square-integrable for μ\muμ, and is defined to be 000 otherwise.
  • ∥h∥μ\|h\|_\mu∥h∥μ​ denotes the quantity sup⁡{ ℓ(h):ℓ∈B∗, Cμ(ℓ,ℓ)≤1 }\sup \{\, \ell(h) : \ell \in B^{*},\ C_\mu(\ell,\ell) \le 1 \,\}sup{ℓ(h):ℓ∈B∗, Cμ​(ℓ,ℓ)≤1}, computed in [0,∞][0,\infty][0,∞] with each real value ℓ(h)\ell(h)ℓ(h) first replaced by max⁡(ℓ(h),0)\max(\ell(h),0)max(ℓ(h),0). The supremum ranges over all ℓ\ellℓ satisfying the constraint; ℓ=0\ell = 0ℓ=0 always satisfies it, so ∥h∥μ≥0\|h\|_\mu \ge 0∥h∥μ​≥0 always and ∥0∥μ=0\|0\|_\mu = 0∥0∥μ​=0.

Statement. Let μ\muμ be a measure on BBB that is Gaussian in the above sense and satisfies ∫Bx μ(dx)=0\int_B x \, \mu(dx) = 0∫B​xμ(dx)=0 (the Bochner integral of the identity map vanishes). Let h∈Bh \in Bh∈B be arbitrary. Then the following two assertions are equivalent:

  1. the push-forward of μ\muμ under the translation x↦x+hx \mapsto x + hx↦x+h is absolutely continuous with respect to μ\muμ, i.e. every μ\muμ-null Borel set is null for the translated measure;
  2. ∥h∥μ≠∞\|h\|_\mu \neq \infty∥h∥μ​=∞, i.e. sup⁡{ℓ(h):ℓ∈B∗, Cμ(ℓ,ℓ)≤1}\sup\{\ell(h) : \ell \in B^{*},\ C_\mu(\ell,\ell)\le 1\}sup{ℓ(h):ℓ∈B∗, Cμ​(ℓ,ℓ)≤1} is finite.

The claim is a biconditional, so both directions are asserted. The quantifier over hhh is universal and unrestricted: h=0h = 0h=0 is included (there both sides hold, the translation being the identity), and so is the case where μ\muμ is a Dirac mass at 000 (there Cμ≡0C_\mu \equiv 0Cμ​≡0, so the right-hand side holds only for h=0h = 0h=0).

Nothing stronger than absolute continuity is claimed in direction 1 — equivalence of the two measures, or an explicit density, is not part of the statement — and nothing is said about mutual singularity in the failing case.

Human review
  • Endorsed by Shuze Chen · Sep 13, 2026

  • Endorsed by Lucas · Sep 13, 2026

    Confirmed by the mission captain (proposal self-audit).

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