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resample_measure_preserving

Proved

by allychan327 · Jun 24, 2026 · Mathlib c5ea003 (Lean v4.30.0)

concentration-inequalitiesefron-steinprobabilityproduct-measureresamplingvariance

The single-coordinate resample map is measure-preserving on a product cube. Let μ=⨂iμi\mu = \bigotimes_i \mu_iμ=⨂i​μi​ be a product of probability measures on ∏iαi\prod_i \alpha_i∏i​αi​ (finite index set), and fix a coordinate iii. The map (ω,ω′)↦ω(i←ωi′)(\omega, \omega') \mapsto \omega^{(i \leftarrow \omega'_i)}(ω,ω′)↦ω(i←ωi′​), which replaces the iii-th coordinate of ω\omegaω by ωi′\omega'_iωi′​ (leaving the other coordinates of ω\omegaω unchanged), pushes the product measure μ⊗μ\mu \otimes \muμ⊗μ forward to μ\muμ. Equivalently: resampling one coordinate of a draw from a product measure, using an independent copy, leaves the distribution unchanged. This is the measure-theoretic backbone of the resampling form of the Efron–Stein inequality: it makes Z(ω)Z(\omega)Z(ω) and Z(ω(i←ωi′))Z(\omega^{(i \leftarrow \omega'_i)})Z(ω(i←ωi′​)) identically distributed, so the resampled difference Z−Zi′Z - Z'_iZ−Zi′​ is well-behaved.

Preamble
import Mathlib.Probability.CondVar
import Mathlib.Probability.Moments.Variance
import Mathlib.Probability.Independence.Basic
import Mathlib.MeasureTheory.Constructions.Pi
import Mathlib.Probability.Independence.Integration
import Mathlib.Probability.IdentDistrib
open MeasureTheory ProbabilityTheory Filter Set Function
open scoped ENNReal NNReal BigOperators
Formal statement
theorem resample_measure_preserving
    {ι : Type*} [Fintype ι] [DecidableEq ι]
    {α : ι → Type*} [∀ i, MeasurableSpace (α i)]
    (μ : ∀ i, Measure (α i)) [∀ i, IsProbabilityMeasure (μ i)] (i : ι) :
    MeasurePreserving (fun p : (∀ j, α j) × (∀ j, α j) => Function.update p.1 i (p.2 i))
      ((Measure.pi μ).prod (Measure.pi μ)) (Measure.pi μ) := by sorry
Source
R. van Handel, Probability in High Dimension (APC 550 lecture notes, Princeton), §2.1 Tensorization and bounded differences (Efron-Stein via the resampling / symmetrization identity Var(W)=½E[(W-W')^2] for an independent copy); Boucheron-Lugosi-Massart, Concentration Inequalities (OUP 2013), Ch. 3, Theorem 3.1 (the resampling form of the Efron-Stein inequality on a product space).

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