Documentation

Mathlib.Probability.StrongLaw

The strong law of large numbers #

We prove the strong law of large numbers, in ProbabilityTheory.strong_law_ae: If X n is a sequence of independent identically distributed integrable random variables, then βˆ‘ i ∈ range n, X i / n converges almost surely to 𝔼[X 0]. We give here the strong version, due to Etemadi, that only requires pairwise independence.

This file also contains the Lα΅– version of the strong law of large numbers provided by ProbabilityTheory.strong_law_Lp which shows βˆ‘ i ∈ range n, X i / n converges in Lα΅– to 𝔼[X 0] provided X n is independent identically distributed and is Lα΅–.

Implementation #

The main point is to prove the result for real-valued random variables, as the general case of Banach-space valued random variables follows from this case and approximation by simple functions. The real version is given in ProbabilityTheory.strong_law_ae_real.

We follow the proof by Etemadi [Etemadi, An elementary proof of the strong law of large numbers][etemadi_strong_law], which goes as follows.

It suffices to prove the result for nonnegative X, as one can prove the general result by splitting a general X into its positive part and negative part. Consider Xβ‚™ a sequence of nonnegative integrable identically distributed pairwise independent random variables. Let Yβ‚™ be the truncation of Xβ‚™ up to n. We claim that

  βˆ‘_k β„™ (|βˆ‘_{i=0}^{c^k - 1} Yα΅’ - 𝔼[Yα΅’]| > c^k Ξ΅)
    ≀ βˆ‘_k (c^k Ξ΅)^{-2} βˆ‘_{i=0}^{c^k - 1} Var[Yα΅’]    (by Markov inequality)
    ≀ βˆ‘_i (C/i^2) Var[Yα΅’]                           (as βˆ‘_{c^k > i} 1/(c^k)^2 ≀ C/i^2)
    ≀ βˆ‘_i (C/i^2) 𝔼[Yα΅’^2]
    ≀ 2C 𝔼[X^2]                                     (see `sum_variance_truncation_le`)

Prerequisites on truncations #

def ProbabilityTheory.truncation {Ξ± : Type u_1} (f : Ξ± β†’ ℝ) (A : ℝ) :
Ξ± β†’ ℝ

Truncating a real-valued function to the interval (-A, A].

Equations
Instances For
    theorem ProbabilityTheory.abs_truncation_le_bound {Ξ± : Type u_1} (f : Ξ± β†’ ℝ) (A : ℝ) (x : Ξ±) :
    @[simp]
    theorem ProbabilityTheory.truncation_zero {Ξ± : Type u_1} (f : Ξ± β†’ ℝ) :
    theorem ProbabilityTheory.abs_truncation_le_abs_self {Ξ± : Type u_1} (f : Ξ± β†’ ℝ) (A : ℝ) (x : Ξ±) :
    theorem ProbabilityTheory.truncation_eq_self {Ξ± : Type u_1} {f : Ξ± β†’ ℝ} {A : ℝ} {x : Ξ±} (h : |f x| < A) :
    theorem ProbabilityTheory.truncation_eq_of_nonneg {Ξ± : Type u_1} {f : Ξ± β†’ ℝ} {A : ℝ} (h : βˆ€ (x : Ξ±), 0 ≀ f x) :
    ProbabilityTheory.truncation f A = (Set.Ioc 0 A).indicator id ∘ f
    theorem ProbabilityTheory.truncation_nonneg {Ξ± : Type u_1} {f : Ξ± β†’ ℝ} (A : ℝ) {x : Ξ±} (h : 0 ≀ f x) :
    theorem ProbabilityTheory.moment_truncation_eq_intervalIntegral {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.AEStronglyMeasurable f ΞΌ) {A : ℝ} (hA : 0 ≀ A) {n : β„•} (hn : n β‰  0) :
    ∫ (x : Ξ±), ProbabilityTheory.truncation f A x ^ n βˆ‚ΞΌ = ∫ (y : ℝ) in -A..A, y ^ n βˆ‚MeasureTheory.Measure.map f ΞΌ
    theorem ProbabilityTheory.moment_truncation_eq_intervalIntegral_of_nonneg {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.AEStronglyMeasurable f ΞΌ) {A : ℝ} {n : β„•} (hn : n β‰  0) (h'f : 0 ≀ f) :
    ∫ (x : Ξ±), ProbabilityTheory.truncation f A x ^ n βˆ‚ΞΌ = ∫ (y : ℝ) in 0 ..A, y ^ n βˆ‚MeasureTheory.Measure.map f ΞΌ
    theorem ProbabilityTheory.integral_truncation_eq_intervalIntegral {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.AEStronglyMeasurable f ΞΌ) {A : ℝ} (hA : 0 ≀ A) :
    ∫ (x : Ξ±), ProbabilityTheory.truncation f A x βˆ‚ΞΌ = ∫ (y : ℝ) in -A..A, y βˆ‚MeasureTheory.Measure.map f ΞΌ
    theorem ProbabilityTheory.integral_truncation_eq_intervalIntegral_of_nonneg {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.AEStronglyMeasurable f ΞΌ) {A : ℝ} (h'f : 0 ≀ f) :
    ∫ (x : Ξ±), ProbabilityTheory.truncation f A x βˆ‚ΞΌ = ∫ (y : ℝ) in 0 ..A, y βˆ‚MeasureTheory.Measure.map f ΞΌ
    theorem ProbabilityTheory.integral_truncation_le_integral_of_nonneg {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.Integrable f ΞΌ) (h'f : 0 ≀ f) {A : ℝ} :
    ∫ (x : Ξ±), ProbabilityTheory.truncation f A x βˆ‚ΞΌ ≀ ∫ (x : Ξ±), f x βˆ‚ΞΌ
    theorem ProbabilityTheory.tendsto_integral_truncation {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {f : Ξ± β†’ ℝ} (hf : MeasureTheory.Integrable f ΞΌ) :
    Filter.Tendsto (fun (A : ℝ) => ∫ (x : Ξ±), ProbabilityTheory.truncation f A x βˆ‚ΞΌ) Filter.atTop (nhds (∫ (x : Ξ±), f x βˆ‚ΞΌ))

    If a function is integrable, then the integral of its truncated versions converges to the integral of the whole function.

    theorem ProbabilityTheory.IdentDistrib.truncation {Ξ± : Type u_1} {m : MeasurableSpace Ξ±} {ΞΌ : MeasureTheory.Measure Ξ±} {Ξ² : Type u_2} [MeasurableSpace Ξ²] {Ξ½ : MeasureTheory.Measure Ξ²} {f : Ξ± β†’ ℝ} {g : Ξ² β†’ ℝ} (h : ProbabilityTheory.IdentDistrib f g ΞΌ Ξ½) {A : ℝ} :
    theorem ProbabilityTheory.sum_prob_mem_Ioc_le {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] {X : Ξ© β†’ ℝ} (hint : MeasureTheory.Integrable X MeasureTheory.volume) (hnonneg : 0 ≀ X) {K : β„•} {N : β„•} (hKN : K ≀ N) :
    βˆ‘ j ∈ Finset.range K, MeasureTheory.volume {Ο‰ : Ξ© | X Ο‰ ∈ Set.Ioc ↑j ↑N} ≀ ENNReal.ofReal ((∫ (a : Ξ©), X a) + 1)
    theorem ProbabilityTheory.tsum_prob_mem_Ioi_lt_top {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] {X : Ξ© β†’ ℝ} (hint : MeasureTheory.Integrable X MeasureTheory.volume) (hnonneg : 0 ≀ X) :
    βˆ‘' (j : β„•), MeasureTheory.volume {Ο‰ : Ξ© | X Ο‰ ∈ Set.Ioi ↑j} < ⊀
    theorem ProbabilityTheory.sum_variance_truncation_le {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] {X : Ξ© β†’ ℝ} (hint : MeasureTheory.Integrable X MeasureTheory.volume) (hnonneg : 0 ≀ X) (K : β„•) :
    βˆ‘ j ∈ Finset.range K, (↑j ^ 2)⁻¹ * ∫ (a : Ξ©), (ProbabilityTheory.truncation X ↑j ^ 2) a ≀ 2 * ∫ (a : Ξ©), X a

    Proof of the strong law of large numbers (almost sure version, assuming only pairwise independence) for nonnegative random variables, following Etemadi's proof.

    theorem ProbabilityTheory.strong_law_aux1 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) {c : ℝ} (c_one : 1 < c) {Ξ΅ : ℝ} (Ξ΅pos : 0 < Ξ΅) :
    βˆ€α΅ (Ο‰ : Ξ©), βˆ€αΆ  (n : β„•) in Filter.atTop, |βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, ProbabilityTheory.truncation (X i) (↑i) Ο‰ - ∫ (a : Ξ©), (βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, ProbabilityTheory.truncation (X i) ↑i) a| < Ξ΅ * β†‘βŒŠc ^ nβŒ‹β‚Š

    The truncation of Xα΅’ up to i satisfies the strong law of large numbers (with respect to the truncated expectation) along the sequence c^n, for any c > 1, up to a given Ξ΅ > 0. This follows from a variance control.

    theorem ProbabilityTheory.strong_law_aux2 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) {c : ℝ} (c_one : 1 < c) :
    βˆ€α΅ (Ο‰ : Ξ©), (fun (n : β„•) => βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, ProbabilityTheory.truncation (X i) (↑i) Ο‰ - ∫ (a : Ξ©), (βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, ProbabilityTheory.truncation (X i) ↑i) a) =o[Filter.atTop] fun (n : β„•) => β†‘βŒŠc ^ nβŒ‹β‚Š
    theorem ProbabilityTheory.strong_law_aux3 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) :
    (fun (n : β„•) => (∫ (a : Ξ©), (βˆ‘ i ∈ Finset.range n, ProbabilityTheory.truncation (X i) ↑i) a) - ↑n * ∫ (a : Ξ©), X 0 a) =o[Filter.atTop] Nat.cast

    The expectation of the truncated version of Xα΅’ behaves asymptotically like the whole expectation. This follows from convergence and CesΓ ro averaging.

    theorem ProbabilityTheory.strong_law_aux4 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) {c : ℝ} (c_one : 1 < c) :
    βˆ€α΅ (Ο‰ : Ξ©), (fun (n : β„•) => βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, ProbabilityTheory.truncation (X i) (↑i) Ο‰ - β†‘βŒŠc ^ nβŒ‹β‚Š * ∫ (a : Ξ©), X 0 a) =o[Filter.atTop] fun (n : β„•) => β†‘βŒŠc ^ nβŒ‹β‚Š
    theorem ProbabilityTheory.strong_law_aux5 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) :
    βˆ€α΅ (Ο‰ : Ξ©), (fun (n : β„•) => βˆ‘ i ∈ Finset.range n, ProbabilityTheory.truncation (X i) (↑i) Ο‰ - βˆ‘ i ∈ Finset.range n, X i Ο‰) =o[Filter.atTop] fun (n : β„•) => ↑n

    The truncated and non-truncated versions of Xα΅’ have the same asymptotic behavior, as they almost surely coincide at all but finitely many steps. This follows from a probability computation and Borel-Cantelli.

    theorem ProbabilityTheory.strong_law_aux6 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) {c : ℝ} (c_one : 1 < c) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (βˆ‘ i ∈ Finset.range ⌊c ^ nβŒ‹β‚Š, X i Ο‰) / β†‘βŒŠc ^ nβŒ‹β‚Š) Filter.atTop (nhds (∫ (a : Ξ©), X 0 a))
    theorem ProbabilityTheory.strong_law_aux7 {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (hnonneg : βˆ€ (i : β„•) (Ο‰ : Ξ©), 0 ≀ X i Ο‰) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (βˆ‘ i ∈ Finset.range n, X i Ο‰) / ↑n) Filter.atTop (nhds (∫ (a : Ξ©), X 0 a))

    Xα΅’ satisfies the strong law of large numbers along all integers. This follows from the corresponding fact along the sequences c^n, and the fact that any integer can be sandwiched between c^n and c^(n+1) with comparably small error if c is close enough to 1 (which is formalized in tendsto_div_of_monotone_of_tendsto_div_floor_pow).

    theorem ProbabilityTheory.strong_law_ae_real {Ξ© : Type u_2} [MeasureTheory.MeasureSpace Ξ©] (X : β„• β†’ Ξ© β†’ ℝ) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (βˆ‘ i ∈ Finset.range n, X i Ο‰) / ↑n) Filter.atTop (nhds (∫ (a : Ξ©), X 0 a))

    Strong law of large numbers, almost sure version: if X n is a sequence of independent identically distributed integrable real-valued random variables, then βˆ‘ i ∈ range n, X i / n converges almost surely to 𝔼[X 0]. We give here the strong version, due to Etemadi, that only requires pairwise independence. Superseded by strong_law_ae, which works for random variables taking values in any Banach space.

    theorem ProbabilityTheory.strong_law_ae_simpleFunc_comp {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] [MeasurableSpace E] (X : β„• β†’ Ξ© β†’ E) (h' : Measurable (X 0)) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) (Ο† : MeasureTheory.SimpleFunc E E) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (↑n)⁻¹ β€’ βˆ‘ i ∈ Finset.range n, Ο† (X i Ο‰)) Filter.atTop (nhds (∫ (a : Ξ©), (⇑φ ∘ X 0) a))

    Preliminary lemma for the strong law of large numbers for vector-valued random variables: the composition of the random variables with a simple function satisfies the strong law of large numbers.

    theorem ProbabilityTheory.strong_law_ae_of_measurable {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] [MeasureTheory.IsProbabilityMeasure MeasureTheory.volume] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] [MeasurableSpace E] [BorelSpace E] (X : β„• β†’ Ξ© β†’ E) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (h' : MeasureTheory.StronglyMeasurable (X 0)) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (↑n)⁻¹ β€’ βˆ‘ i ∈ Finset.range n, X i Ο‰) Filter.atTop (nhds (∫ (a : Ξ©), X 0 a))

    Preliminary lemma for the strong law of large numbers for vector-valued random variables, assuming measurability in addition to integrability. This is weakened to ae measurability in the full version ProbabilityTheory.strong_law_ae.

    theorem ProbabilityTheory.strong_law_ae {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] [MeasurableSpace E] [BorelSpace E] {Ξ© : Type u_3} [MeasureTheory.MeasureSpace Ξ©] (X : β„• β†’ Ξ© β†’ E) (hint : MeasureTheory.Integrable (X 0) MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) :
    βˆ€α΅ (Ο‰ : Ξ©), Filter.Tendsto (fun (n : β„•) => (↑n)⁻¹ β€’ βˆ‘ i ∈ Finset.range n, X i Ο‰) Filter.atTop (nhds (∫ (a : Ξ©), X 0 a))

    Strong law of large numbers, almost sure version: if X n is a sequence of independent identically distributed integrable random variables taking values in a Banach space, then n⁻¹ β€’ βˆ‘ i ∈ range n, X i converges almost surely to 𝔼[X 0]. We give here the strong version, due to Etemadi, that only requires pairwise independence.

    theorem ProbabilityTheory.strong_law_Lp {Ξ© : Type u_1} [MeasureTheory.MeasureSpace Ξ©] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] [MeasurableSpace E] [BorelSpace E] {p : ENNReal} (hp : 1 ≀ p) (hp' : p β‰  ⊀) (X : β„• β†’ Ξ© β†’ E) (hβ„’p : MeasureTheory.Memβ„’p (X 0) p MeasureTheory.volume) (hindep : Pairwise fun (i j : β„•) => ProbabilityTheory.IndepFun (X i) (X j) MeasureTheory.volume) (hident : βˆ€ (i : β„•), ProbabilityTheory.IdentDistrib (X i) (X 0) MeasureTheory.volume MeasureTheory.volume) :
    Filter.Tendsto (fun (n : β„•) => MeasureTheory.eLpNorm (fun (Ο‰ : Ξ©) => (↑n)⁻¹ β€’ βˆ‘ i ∈ Finset.range n, X i Ο‰ - ∫ (a : Ξ©), X 0 a) p MeasureTheory.volume) Filter.atTop (nhds 0)

    Strong law of large numbers, Lα΅– version: if X n is a sequence of independent identically distributed random variables in Lα΅–, then n⁻¹ β€’ βˆ‘ i ∈ range n, X i converges in Lα΅– to 𝔼[X 0].