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Project-declaredLean 4.33.0-rc1 · mathlib@0434c033

Measure Real exists abs lt le

ProbabilityTheory.measureReal_exists_abs_lt_le

Plain-language statement

Maximal inequality: the probability that |X| exceeds lam somewhere on a finite set F ⊆ Iic t is at most K / lam, with K independent of F.

Exact Lean statement

lemma measureReal_exists_abs_lt_le [OrderBot ι] [IsFiniteMeasure μ]
    (hX : StronglyAdapted 𝓕 X) (hXint : ∀ s, Integrable (X s) μ) {C : ℝ}
    (hC : ∀ S : ElementaryPredictableSet 𝓕, μ[(S.indicator (1 : ℝ) ● X) t] ≤ C)
    (hlam : 0 < lam) (hF : ∀ s ∈ F, s ≤ t) :
    μ.real {ω | ∃ s ∈ F, lam < |X s ω|}
      ≤ 2 * (C + ∫ ω, |X t ω - X ⊥ ω| ∂μ + ∫ ω, |X ⊥ ω| ∂μ + ∫ ω, |X t ω| ∂μ) / lam

Formal artifact

Lean source

Canonical source
Full Lean sourceLean 4
lemma measureReal_exists_abs_lt_le [OrderBot ι] [IsFiniteMeasure μ]    (hX : StronglyAdapted 𝓕 X) (hXint :  s, Integrable (X s) μ) {C : }    (hC :  S : ElementaryPredictableSet 𝓕, μ[(S.indicator (1 : ) ● X) t]  C)    (hlam : 0 < lam) (hF :  s  F, s  t) :    μ.real {ω |  s  F, lam < |X s ω|}       2 * (C + ∫ ω, |X t ω - X ⊥ ω| ∂μ + ∫ ω, |X ⊥ ω| ∂μ + ∫ ω, |X t ω| ∂μ) / lam := by  set G : Finset ι := insert ⊥ F with hG  have hGF : F  G := subset_insert _ _  have hGle :  s  G, s  t := by simp [G]; grind  set n :  := G.card with hn  set idx :   ι := finIdx G t with hidx  have hmono : Monotone idx := finIdx_monotone hGle  have hidx0 : idx 0 =:= finIdx_zero_eq_bot (mem_insert_self _ _)  have hidxn : idx n = t := finIdx_eq_of_card_le hn.ge  -- two-sided expectation bound for adapted 0/1 weights along `idx`  have hbdd :  W :   Ω  , ( k, k < n   ω, W k ω = 0  W k ω = 1)       ( k, k < n  Measurable[𝓕 (idx k)] (W k))       ∫ ω, (∑ k  range n, W k ω * (X (idx (k + 1)) ω - X (idx k) ω)) ∂μ         Set.Icc (-(C + ∫ ω, |X (idx n) ω - X (idx 0) ω| ∂μ)) C :=    fun W hW01 hWmeas  integral_sum_weight_increments_mem_Icc (t := t) hXint hC hmono      (finIdx_le hGle) hW01 hWmeas  -- apply the one-sided maximal inequality to `X`  have hpos := mul_measureReal_exists_lt_le (lam := lam) hX hXint (n := n) hmono (K := C)    (fun W hW01 hWmeas  (hbdd W hW01 hWmeas).2)  -- and to `-X`  have hnegX : StronglyAdapted 𝓕 (fun s ω  -X s ω) := fun s  (hX s).neg  have hnegint :  s, Integrable ((fun s ω  -X s ω) s) μ := fun s  (hXint s).neg  have hneg := mul_measureReal_exists_lt_le (lam := lam) hnegX hnegint (n := n) hmono    (K := C + ∫ ω, |X (idx n) ω - X (idx 0) ω| ∂μ)    (fun W hW01 hWmeas  by      have h1 := (hbdd W hW01 hWmeas).1      have h2 : ∫ ω, (∑ k  range n, W k ω * (-X (idx (k + 1)) ω - -X (idx k) ω)) ∂μ          = -∫ ω, (∑ k  range n, W k ω * (X (idx (k + 1)) ω - X (idx k) ω)) ∂μ := by        rw [ integral_neg]        congr 1 with ω        rw [ sum_neg_distrib]        grind      rw [h2]      linarith)  -- combine the two events  have hsub : {ω |  s  F, lam < |X s ω|}       {ω |  k  n, lam < X (idx k) ω} ∪ {ω |  k  n, lam < -X (idx k) ω} := by    rintro ω s, hs, habs    obtain k, hk, hkeq := exists_finIdx_eq (t := t) (hGF hs)    grind  have habs u : ∫ ω, |(fun s ω  -X s ω) u ω| ∂μ = ∫ ω, |X u ω| ∂μ := by simp  rw [hidx0, hidxn] at hpos hneg  rw [habs ⊥, habs t] at hneg  have hunion : μ.real {ω |  s  F, lam < |X s ω|}       μ.real {ω |  k  n, lam < X (idx k) ω} + μ.real {ω |  k  n, lam < -X (idx k) ω} :=    (measureReal_mono hsub).trans (measureReal_union_le _ _)  have hI : (0 : )  ∫ ω, |X t ω - X ⊥ ω| ∂μ := integral_nonneg fun ω  abs_nonneg _  rw [le_div_iff₀ hlam]  have hmul : lam * μ.real {ω |  s  F, lam < |X s ω|}       lam * (μ.real {ω |  k  n, lam < X (idx k) ω}        + μ.real {ω |  k  n, lam < -X (idx k) ω}) :=    mul_le_mul_of_nonneg_left hunion hlam.le  rw [mul_add] at hmul  nlinarith [hpos, hneg, hmul]
Project
Brownian motion
License
Apache-2.0
Commit
5077304f5e73
Source
BrownianMotion/StochasticIntegral/Quasimartingale/MaximalInequality.lean:653-711

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probabilitystochastic processesmeasure theory

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Project-declaredLean 4.33.0-rc1

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Project documentation

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probabilitystochastic processesmeasure theory

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Person-level attribution pending.

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