LeanMachineLearning

ProbabilityTheory.HasSubexponentialMGF.measure_abs_average_ge_le🔗

Lemma

From the authors

Tail bound for an average of independent sub-exponential variables with parameters (V i, b): writing K for the number of variables and V̄ := (∑ i, V i) / K, P(|(∑ i, X i) / K| ≥ t) ≤ 2 exp (-K min (t ^ 2 / (2 V̄)) (t / (2 b))).

Types
  • Ω : Type u_1mΩ : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra.
  • ι : Type u_2Fintype ιFintype α means that α is finite, i.e. there are only finitely many distinct elements of type α.Nonempty ι
Given
  • μ : MeasureTheory.Measure ΩA measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.
  • b :
  • X : ι → Ω →
  • V : ι →
  • t :
Assuming
Then
μ.real {ω | t|(∑ i, X i ω) / ↑(Fintype.card ι)|}2 * Real.exp (-(↑(Fintype.card ι) * min (t ^ 2 / (2 * ((∑ i, V i) / ↑(Fintype.card ι)))) (t / (2 * b))))
Code
lemma measure_abs_average_ge_le {ι : Type*} [Fintype ι] [Nonempty ι] {X : ι → Ω → ℝ}
    (h_indep : iIndepFun X μ) {V : ι → ℝ} (h : ∀ i, HasSubexponentialMGF (X i) (V i) b μ)
    {t : ℝ} (ht : 0 ≤ t) :
    μ.real {ω | t ≤ |(∑ i, X i ω) / Fintype.card ι|}
      ≤ 2 * exp (-(Fintype.card ι
        * min (t ^ 2 / (2 * ((∑ i, V i) / Fintype.card ι))) (t / (2 * b))))
Proof
by
  have hK : (0 : ℝ) < Fintype.card ι := by
    have := Fintype.card_pos (α := ι)
    positivity
  have hbound := (average_of_iIndepFun h_indep h).measure_abs_ge_le ht
  refine hbound.trans (le_of_eq ?_)
  congr 3
  rw [(monotone_mul_left_of_nonneg hK.le).map_min]
  congr 1
  · field_simp
  · field_simp

Meaning last changed in v4.34.0-rc2-76-g565f652 (2026-09-10).

Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file · open it in the Lean web editor.

Dependency graph

Audit surface: 1 project declarations, 69 external constants

✓ Proved: no sorry anywhere in its closure

This is the tool's own reading of one build's recorded axioms, and it is not robust against an author who wants it to pass. Checking meant to be relied on should go through Comparator, which replays the proof through the kernel from an export against an explicit list of permitted axioms.