Covariance Cauchy–Schwarz Inequality
ProvedFunctionalInequalities.Probability.covariance_sq_le_variance_mul_variance_readablebkl2026functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This statement gives the covariance form of the Cauchy–Schwarz inequality for square-integrable real random variables.
Let be a probability space and let satisfy , expressed by MemLp X 2 μ and MemLp Y 2 μ. Then
This is a general measure-theoretic inequality on an arbitrary measurable space and is used as a deterministic covariance estimate in posterior calculations.
Original Lean declaration header
Within namespace FunctionalInequalities.Probability. Shown in its original module context. Proof omitted.
theorem covariance_sq_le_variance_mul_variance {X Y : Ω → ℝ} (hX : MemLp X 2 μ) (hY : MemLp Y 2 μ) :
covariance X Y μ ^ 2 ≤ variance X μ * variance Y μ
Module navigation
Module: Probability/Covariance/CauchySchwarz.
Source excerpt, lines 23–24 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_Covariance_CauchySchwarz
open MeasureTheory ProbabilityTheory
variable {Ω : Type*} [MeasurableSpace Ω] {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ]
Formal statement
theorem FunctionalInequalities.Probability.covariance_sq_le_variance_mul_variance_readable {X Y : Ω → ℝ} (hX : MemLp X 2 μ) (hY : MemLp Y 2 μ) :
covariance X Y μ ^ 2 ≤ variance X μ * variance Y μ := by sorry
Source
Source: Bizeul–Klartag–Lehec, Presenting a proof of the Kannan–Lovász–Simonovits conjecture (arXiv:2610.05474v1), Equation (67), p. 19 (unnumbered display) and the compact stage before it. Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Probability/Covariance/CauchySchwarz.lean#L22-L71 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Probability.Covariance.CauchySchwarz`, declaration `FunctionalInequalities.Probability.covariance_sq_le_variance_mul_variance`; the repository is private at upload time).