[Merged by Bors] - feat: port Probability.Moments

Mathlib.lean

@@ -2475,6 +2475,7 @@ import Mathlib.Probability.Martingale.Convergence
 import Mathlib.Probability.Martingale.OptionalSampling
 import Mathlib.Probability.Martingale.OptionalStopping
 import Mathlib.Probability.Martingale.Upcrossing
+import Mathlib.Probability.Moments
 import Mathlib.Probability.Notation
 import Mathlib.Probability.ProbabilityMassFunction.Basic
 import Mathlib.Probability.ProbabilityMassFunction.Constructions

Mathlib/Probability/Moments.lean

@@ -0,0 +1,386 @@
+/-
+Copyright (c) 2022 Rémy Degenne. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Rémy Degenne
+
+! This file was ported from Lean 3 source module probability.moments
+! leanprover-community/mathlib commit 85453a2a14be8da64caf15ca50930cf4c6e5d8de
+! Please do not edit these lines, except to modify the commit id
+! if you have ported upstream changes.
+-/
+import Mathlib.Probability.Variance
+
+/-!
+# Moments and moment generating function
+
+## Main definitions
+
+* `ProbabilityTheory.moment X p μ`: `p`th moment of a real random variable `X` with respect to
+  measure `μ`, `μ[X^p]`
+* `ProbabilityTheory.centralMoment X p μ`:`p`th central moment of `X` with respect to measure `μ`,
+  `μ[(X - μ[X])^p]`
+* `ProbabilityTheory.mgf X μ t`: moment generating function of `X` with respect to measure `μ`,
+  `μ[exp(t*X)]`
+* `ProbabilityTheory.cgf X μ t`: cumulant generating function, logarithm of the moment generating
+  function
+
+## Main results
+
+* `ProbabilityTheory.IndepFun.mgf_add`: if two real random variables `X` and `Y` are independent
+  and their mgfs are defined at `t`, then `mgf (X + Y) μ t = mgf X μ t * mgf Y μ t`
+* `ProbabilityTheory.IndepFun.cgf_add`: if two real random variables `X` and `Y` are independent
+  and their cgfs are defined at `t`, then `cgf (X + Y) μ t = cgf X μ t + cgf Y μ t`
+* `ProbabilityTheory.measure_ge_le_exp_cgf` and `ProbabilityTheory.measure_le_le_exp_cgf`:
+  Chernoff bound on the upper (resp. lower) tail of a random variable. For `t` nonnegative such that
+  the cgf exists, `ℙ(ε ≤ X) ≤ exp(- t*ε + cgf X ℙ t)`. See also
+  `ProbabilityTheory.measure_ge_le_exp_mul_mgf` and
+  `ProbabilityTheory.measure_le_le_exp_mul_mgf` for versions of these results using `mgf` instead
+  of `cgf`.
+
+-/
+
+
+open MeasureTheory Filter Finset Real
+
+noncomputable section
+
+open scoped BigOperators MeasureTheory ProbabilityTheory ENNReal NNReal
+
+namespace ProbabilityTheory
+
+variable {Ω ι : Type _} {m : MeasurableSpace Ω} {X : Ω → ℝ} {p : ℕ} {μ : Measure Ω}
+
+/-- Moment of a real random variable, `μ[X ^ p]`. -/
+def moment (X : Ω → ℝ) (p : ℕ) (μ : Measure Ω) : ℝ :=
+  μ[X ^ p]
+#align probability_theory.moment ProbabilityTheory.moment
+
+/-- Central moment of a real random variable, `μ[(X - μ[X]) ^ p]`. -/
+def centralMoment (X : Ω → ℝ) (p : ℕ) (μ : Measure Ω) : ℝ := by
+  have m := fun (x : Ω) => μ[X] -- Porting note: Lean deems `μ[(X - fun x => μ[X]) ^ p]` ambiguous
+  exact μ[(X - m) ^ p]
+#align probability_theory.central_moment ProbabilityTheory.centralMoment
+
+@[simp]
+theorem moment_zero (hp : p ≠ 0) : moment 0 p μ = 0 := by
+  simp only [moment, hp, zero_pow', Ne.def, not_false_iff, Pi.zero_apply, integral_const,
+    smul_eq_mul, mul_zero]
+#align probability_theory.moment_zero ProbabilityTheory.moment_zero
+
+@[simp]
+theorem centralMoment_zero (hp : p ≠ 0) : centralMoment 0 p μ = 0 := by
+  simp only [centralMoment, hp, Pi.zero_apply, integral_const, smul_eq_mul,
+    mul_zero, zero_sub, Pi.pow_apply, Pi.neg_apply, neg_zero, zero_pow', Ne.def, not_false_iff]
+#align probability_theory.central_moment_zero ProbabilityTheory.centralMoment_zero
+
+theorem centralMoment_one' [IsFiniteMeasure μ] (h_int : Integrable X μ) :
+    centralMoment X 1 μ = (1 - (μ Set.univ).toReal) * μ[X] := by
+  simp only [centralMoment, Pi.sub_apply, pow_one]
+  rw [integral_sub h_int (integrable_const _)]
+  simp only [sub_mul, integral_const, smul_eq_mul, one_mul]
+#align probability_theory.central_moment_one' ProbabilityTheory.centralMoment_one'
+
+@[simp]
+theorem centralMoment_one [IsProbabilityMeasure μ] : centralMoment X 1 μ = 0 := by
+  by_cases h_int : Integrable X μ
+  · rw [centralMoment_one' h_int]
+    simp only [measure_univ, ENNReal.one_toReal, sub_self, zero_mul]
+  · simp only [centralMoment, Pi.sub_apply, pow_one]
+    have : ¬Integrable (fun x => X x - integral μ X) μ := by
+      refine' fun h_sub => h_int _
+      have h_add : X = (fun x => X x - integral μ X) + fun _ => integral μ X := by ext1 x; simp
+      rw [h_add]
+      exact h_sub.add (integrable_const _)
+    rw [integral_undef this]
+#align probability_theory.central_moment_one ProbabilityTheory.centralMoment_one
+
+theorem centralMoment_two_eq_variance [IsFiniteMeasure μ] (hX : Memℒp X 2 μ) :
+    centralMoment X 2 μ = variance X μ := by rw [hX.variance_eq]; rfl
+#align probability_theory.central_moment_two_eq_variance ProbabilityTheory.centralMoment_two_eq_variance
+
+section MomentGeneratingFunction
+
+variable {t : ℝ}
+
+/-- Moment generating function of a real random variable `X`: `fun t => μ[exp(t*X)]`. -/
+def mgf (X : Ω → ℝ) (μ : Measure Ω) (t : ℝ) : ℝ :=
+  μ[fun ω => exp (t * X ω)]
+#align probability_theory.mgf ProbabilityTheory.mgf
+
+/-- Cumulant generating function of a real random variable `X`: `fun t => log μ[exp(t*X)]`. -/
+def cgf (X : Ω → ℝ) (μ : Measure Ω) (t : ℝ) : ℝ :=
+  log (mgf X μ t)
+#align probability_theory.cgf ProbabilityTheory.cgf
+
+@[simp]
+theorem mgf_zero_fun : mgf 0 μ t = (μ Set.univ).toReal := by
+  simp only [mgf, Pi.zero_apply, mul_zero, exp_zero, integral_const, smul_eq_mul, mul_one]
+#align probability_theory.mgf_zero_fun ProbabilityTheory.mgf_zero_fun
+
+@[simp]
+theorem cgf_zero_fun : cgf 0 μ t = log (μ Set.univ).toReal := by simp only [cgf, mgf_zero_fun]
+#align probability_theory.cgf_zero_fun ProbabilityTheory.cgf_zero_fun
+
+@[simp]
+theorem mgf_zero_measure : mgf X (0 : Measure Ω) t = 0 := by simp only [mgf, integral_zero_measure]
+#align probability_theory.mgf_zero_measure ProbabilityTheory.mgf_zero_measure
+
+@[simp]
+theorem cgf_zero_measure : cgf X (0 : Measure Ω) t = 0 := by
+  simp only [cgf, log_zero, mgf_zero_measure]
+#align probability_theory.cgf_zero_measure ProbabilityTheory.cgf_zero_measure
+
+@[simp]
+theorem mgf_const' (c : ℝ) : mgf (fun _ => c) μ t = (μ Set.univ).toReal * exp (t * c) := by
+  simp only [mgf, integral_const, smul_eq_mul]
+#align probability_theory.mgf_const' ProbabilityTheory.mgf_const'
+
+-- @[simp] -- Porting note: `simp only` already proves this
+theorem mgf_const (c : ℝ) [IsProbabilityMeasure μ] : mgf (fun _ => c) μ t = exp (t * c) := by
+  simp only [mgf_const', measure_univ, ENNReal.one_toReal, one_mul]
+#align probability_theory.mgf_const ProbabilityTheory.mgf_const
+
+@[simp]
+theorem cgf_const' [IsFiniteMeasure μ] (hμ : μ ≠ 0) (c : ℝ) :
+    cgf (fun _ => c) μ t = log (μ Set.univ).toReal + t * c := by
+  simp only [cgf, mgf_const']
+  rw [log_mul _ (exp_pos _).ne']
+  · rw [log_exp _]
+  · rw [Ne.def, ENNReal.toReal_eq_zero_iff, Measure.measure_univ_eq_zero]
+    simp only [hμ, measure_ne_top μ Set.univ, or_self_iff, not_false_iff]
+#align probability_theory.cgf_const' ProbabilityTheory.cgf_const'
+
+@[simp]
+theorem cgf_const [IsProbabilityMeasure μ] (c : ℝ) : cgf (fun _ => c) μ t = t * c := by
+  simp only [cgf, mgf_const, log_exp]
+#align probability_theory.cgf_const ProbabilityTheory.cgf_const
+
+@[simp]
+theorem mgf_zero' : mgf X μ 0 = (μ Set.univ).toReal := by
+  simp only [mgf, zero_mul, exp_zero, integral_const, smul_eq_mul, mul_one]
+#align probability_theory.mgf_zero' ProbabilityTheory.mgf_zero'
+
+-- @[simp] -- Porting note: `simp only` already proves this
+theorem mgf_zero [IsProbabilityMeasure μ] : mgf X μ 0 = 1 := by
+  simp only [mgf_zero', measure_univ, ENNReal.one_toReal]
+#align probability_theory.mgf_zero ProbabilityTheory.mgf_zero
+
+@[simp]
+theorem cgf_zero' : cgf X μ 0 = log (μ Set.univ).toReal := by simp only [cgf, mgf_zero']
+#align probability_theory.cgf_zero' ProbabilityTheory.cgf_zero'
+
+-- @[simp] -- Porting note: `simp only` already proves this
+theorem cgf_zero [IsProbabilityMeasure μ] : cgf X μ 0 = 0 := by
+  simp only [cgf_zero', measure_univ, ENNReal.one_toReal, log_one]
+#align probability_theory.cgf_zero ProbabilityTheory.cgf_zero
+
+theorem mgf_undef (hX : ¬Integrable (fun ω => exp (t * X ω)) μ) : mgf X μ t = 0 := by
+  simp only [mgf, integral_undef hX]
+#align probability_theory.mgf_undef ProbabilityTheory.mgf_undef
+
+theorem cgf_undef (hX : ¬Integrable (fun ω => exp (t * X ω)) μ) : cgf X μ t = 0 := by
+  simp only [cgf, mgf_undef hX, log_zero]
+#align probability_theory.cgf_undef ProbabilityTheory.cgf_undef
+
+theorem mgf_nonneg : 0 ≤ mgf X μ t := by
+  refine' integral_nonneg _
+  intro ω
+  simp only [Pi.zero_apply]
+  exact (exp_pos _).le
+#align probability_theory.mgf_nonneg ProbabilityTheory.mgf_nonneg
+
+theorem mgf_pos' (hμ : μ ≠ 0) (h_int_X : Integrable (fun ω => exp (t * X ω)) μ) :
+    0 < mgf X μ t := by
+  simp_rw [mgf]
+  have : ∫ x : Ω, exp (t * X x) ∂μ = ∫ x : Ω in Set.univ, exp (t * X x) ∂μ := by
+    simp only [Measure.restrict_univ]
+  rw [this, set_integral_pos_iff_support_of_nonneg_ae _ _]
+  · have h_eq_univ : (Function.support fun x : Ω => exp (t * X x)) = Set.univ := by
+      ext1 x
+      simp only [Function.mem_support, Set.mem_univ, iff_true_iff]
+      exact (exp_pos _).ne'
+    rw [h_eq_univ, Set.inter_univ _]
+    refine' Ne.bot_lt _
+    simp only [hμ, ENNReal.bot_eq_zero, Ne.def, Measure.measure_univ_eq_zero, not_false_iff]
+  · refine' eventually_of_forall fun x => _
+    rw [Pi.zero_apply]
+    exact (exp_pos _).le
+  · rwa [integrableOn_univ]
+#align probability_theory.mgf_pos' ProbabilityTheory.mgf_pos'
+
+theorem mgf_pos [IsProbabilityMeasure μ] (h_int_X : Integrable (fun ω => exp (t * X ω)) μ) :
+    0 < mgf X μ t :=
+  mgf_pos' (IsProbabilityMeasure.ne_zero μ) h_int_X
+#align probability_theory.mgf_pos ProbabilityTheory.mgf_pos
+
+theorem mgf_neg : mgf (-X) μ t = mgf X μ (-t) := by simp_rw [mgf, Pi.neg_apply, mul_neg, neg_mul]
+#align probability_theory.mgf_neg ProbabilityTheory.mgf_neg
+
+theorem cgf_neg : cgf (-X) μ t = cgf X μ (-t) := by simp_rw [cgf, mgf_neg]
+#align probability_theory.cgf_neg ProbabilityTheory.cgf_neg
+
+/-- This is a trivial application of `IndepFun.comp` but it will come up frequently. -/
+theorem IndepFun.exp_mul {X Y : Ω → ℝ} (h_indep : IndepFun X Y μ) (s t : ℝ) :
+    IndepFun (fun ω => exp (s * X ω)) (fun ω => exp (t * Y ω)) μ := by
+  have h_meas : ∀ t, Measurable fun x => exp (t * x) := fun t => (measurable_id'.const_mul t).exp
+  change IndepFun ((fun x => exp (s * x)) ∘ X) ((fun x => exp (t * x)) ∘ Y) μ
+  exact IndepFun.comp h_indep (h_meas s) (h_meas t)
+#align probability_theory.indep_fun.exp_mul ProbabilityTheory.IndepFun.exp_mul
+
+theorem IndepFun.mgf_add {X Y : Ω → ℝ} (h_indep : IndepFun X Y μ)
+    (hX : AEStronglyMeasurable (fun ω => exp (t * X ω)) μ)
+    (hY : AEStronglyMeasurable (fun ω => exp (t * Y ω)) μ) :
+    mgf (X + Y) μ t = mgf X μ t * mgf Y μ t := by
+  simp_rw [mgf, Pi.add_apply, mul_add, exp_add]
+  exact (h_indep.exp_mul t t).integral_mul hX hY
+#align probability_theory.indep_fun.mgf_add ProbabilityTheory.IndepFun.mgf_add
+
+theorem IndepFun.mgf_add' {X Y : Ω → ℝ} (h_indep : IndepFun X Y μ) (hX : AEStronglyMeasurable X μ)
+    (hY : AEStronglyMeasurable Y μ) : mgf (X + Y) μ t = mgf X μ t * mgf Y μ t := by
+  have A : Continuous fun x : ℝ => exp (t * x) := by continuity
+  have h'X : AEStronglyMeasurable (fun ω => exp (t * X ω)) μ :=
+    A.aestronglyMeasurable.comp_aemeasurable hX.aemeasurable
+  have h'Y : AEStronglyMeasurable (fun ω => exp (t * Y ω)) μ :=
+    A.aestronglyMeasurable.comp_aemeasurable hY.aemeasurable
+  exact h_indep.mgf_add h'X h'Y
+#align probability_theory.indep_fun.mgf_add' ProbabilityTheory.IndepFun.mgf_add'
+
+theorem IndepFun.cgf_add {X Y : Ω → ℝ} (h_indep : IndepFun X Y μ)
+    (h_int_X : Integrable (fun ω => exp (t * X ω)) μ)
+    (h_int_Y : Integrable (fun ω => exp (t * Y ω)) μ) :
+    cgf (X + Y) μ t = cgf X μ t + cgf Y μ t := by
+  by_cases hμ : μ = 0
+  · simp [hμ]
+  simp only [cgf, h_indep.mgf_add h_int_X.aestronglyMeasurable h_int_Y.aestronglyMeasurable]
+  exact log_mul (mgf_pos' hμ h_int_X).ne' (mgf_pos' hμ h_int_Y).ne'
+#align probability_theory.indep_fun.cgf_add ProbabilityTheory.IndepFun.cgf_add
+
+theorem aestronglyMeasurable_exp_mul_add {X Y : Ω → ℝ}
+    (h_int_X : AEStronglyMeasurable (fun ω => exp (t * X ω)) μ)
+    (h_int_Y : AEStronglyMeasurable (fun ω => exp (t * Y ω)) μ) :
+    AEStronglyMeasurable (fun ω => exp (t * (X + Y) ω)) μ := by
+  simp_rw [Pi.add_apply, mul_add, exp_add]
+  exact AEStronglyMeasurable.mul h_int_X h_int_Y
+#align probability_theory.ae_strongly_measurable_exp_mul_add ProbabilityTheory.aestronglyMeasurable_exp_mul_add
+
+theorem aestronglyMeasurable_exp_mul_sum {X : ι → Ω → ℝ} {s : Finset ι}
+    (h_int : ∀ i ∈ s, AEStronglyMeasurable (fun ω => exp (t * X i ω)) μ) :
+    AEStronglyMeasurable (fun ω => exp (t * (∑ i in s, X i) ω)) μ := by
+  classical
+  induction' s using Finset.induction_on with i s hi_notin_s h_rec h_int
+  · simp only [Pi.zero_apply, sum_apply, sum_empty, mul_zero, exp_zero]
+    exact aestronglyMeasurable_const
+  · have : ∀ i : ι, i ∈ s → AEStronglyMeasurable (fun ω : Ω => exp (t * X i ω)) μ := fun i hi =>
+      h_int i (mem_insert_of_mem hi)
+    specialize h_rec this
+    rw [sum_insert hi_notin_s]
+    apply aestronglyMeasurable_exp_mul_add (h_int i (mem_insert_self _ _)) h_rec
+#align probability_theory.ae_strongly_measurable_exp_mul_sum ProbabilityTheory.aestronglyMeasurable_exp_mul_sum
+
+theorem IndepFun.integrable_exp_mul_add {X Y : Ω → ℝ} (h_indep : IndepFun X Y μ)
+    (h_int_X : Integrable (fun ω => exp (t * X ω)) μ)
+    (h_int_Y : Integrable (fun ω => exp (t * Y ω)) μ) :
+    Integrable (fun ω => exp (t * (X + Y) ω)) μ := by
+  simp_rw [Pi.add_apply, mul_add, exp_add]
+  exact (h_indep.exp_mul t t).integrable_mul h_int_X h_int_Y
+#align probability_theory.indep_fun.integrable_exp_mul_add ProbabilityTheory.IndepFun.integrable_exp_mul_add
+
+theorem iIndepFun.integrable_exp_mul_sum [IsProbabilityMeasure μ] {X : ι → Ω → ℝ}
+    (h_indep : iIndepFun (fun i => inferInstance) X μ) (h_meas : ∀ i, Measurable (X i))
+    {s : Finset ι} (h_int : ∀ i ∈ s, Integrable (fun ω => exp (t * X i ω)) μ) :
+    Integrable (fun ω => exp (t * (∑ i in s, X i) ω)) μ := by
+  classical
+  induction' s using Finset.induction_on with i s hi_notin_s h_rec h_int
+  · simp only [Pi.zero_apply, sum_apply, sum_empty, mul_zero, exp_zero]
+    exact integrable_const _
+  · have : ∀ i : ι, i ∈ s → Integrable (fun ω : Ω => exp (t * X i ω)) μ := fun i hi =>
+      h_int i (mem_insert_of_mem hi)
+    specialize h_rec this
+    rw [sum_insert hi_notin_s]
+    refine' IndepFun.integrable_exp_mul_add _ (h_int i (mem_insert_self _ _)) h_rec
+    exact (h_indep.indepFun_finset_sum_of_not_mem h_meas hi_notin_s).symm
+set_option linter.uppercaseLean3 false in
+#align probability_theory.Indep_fun.integrable_exp_mul_sum ProbabilityTheory.iIndepFun.integrable_exp_mul_sum
+
+theorem iIndepFun.mgf_sum [IsProbabilityMeasure μ] {X : ι → Ω → ℝ}
+    (h_indep : iIndepFun (fun i => inferInstance) X μ) (h_meas : ∀ i, Measurable (X i))
+    (s : Finset ι) : mgf (∑ i in s, X i) μ t = ∏ i in s, mgf (X i) μ t := by
+  classical
+  induction' s using Finset.induction_on with i s hi_notin_s h_rec h_int
+  · simp only [sum_empty, mgf_zero_fun, measure_univ, ENNReal.one_toReal, prod_empty]
+  · have h_int' : ∀ i : ι, AEStronglyMeasurable (fun ω : Ω => exp (t * X i ω)) μ := fun i =>
+      ((h_meas i).const_mul t).exp.aestronglyMeasurable
+    rw [sum_insert hi_notin_s,
+      IndepFun.mgf_add (h_indep.indepFun_finset_sum_of_not_mem h_meas hi_notin_s).symm (h_int' i)
+        (aestronglyMeasurable_exp_mul_sum fun i _ => h_int' i),
+      h_rec, prod_insert hi_notin_s]
+set_option linter.uppercaseLean3 false in
+#align probability_theory.Indep_fun.mgf_sum ProbabilityTheory.iIndepFun.mgf_sum
+
+theorem iIndepFun.cgf_sum [IsProbabilityMeasure μ] {X : ι → Ω → ℝ}
+    (h_indep : iIndepFun (fun i => inferInstance) X μ) (h_meas : ∀ i, Measurable (X i))
+    {s : Finset ι} (h_int : ∀ i ∈ s, Integrable (fun ω => exp (t * X i ω)) μ) :
+    cgf (∑ i in s, X i) μ t = ∑ i in s, cgf (X i) μ t := by
+  simp_rw [cgf]
+  rw [← log_prod _ _ fun j hj => ?_]
+  · rw [h_indep.mgf_sum h_meas]
+  · exact (mgf_pos (h_int j hj)).ne'
+set_option linter.uppercaseLean3 false in
+#align probability_theory.Indep_fun.cgf_sum ProbabilityTheory.iIndepFun.cgf_sum
+
+/-- **Chernoff bound** on the upper tail of a real random variable. -/
+theorem measure_ge_le_exp_mul_mgf [IsFiniteMeasure μ] (ε : ℝ) (ht : 0 ≤ t)
+    (h_int : Integrable (fun ω => exp (t * X ω)) μ) :
+    (μ {ω | ε ≤ X ω}).toReal ≤ exp (-t * ε) * mgf X μ t := by
+  cases' ht.eq_or_lt with ht_zero_eq ht_pos
+  · rw [ht_zero_eq.symm]
+    simp only [neg_zero, zero_mul, exp_zero, mgf_zero', one_mul]
+    rw [ENNReal.toReal_le_toReal (measure_ne_top μ _) (measure_ne_top μ _)]
+    exact measure_mono (Set.subset_univ _)
+  calc
+    (μ {ω | ε ≤ X ω}).toReal = (μ {ω | exp (t * ε) ≤ exp (t * X ω)}).toReal := by
+      congr with ω
+      simp only [Set.mem_setOf_eq, exp_le_exp, gt_iff_lt]
+      exact ⟨fun h => mul_le_mul_of_nonneg_left h ht_pos.le,
+        fun h => le_of_mul_le_mul_left h ht_pos⟩
+    _ ≤ (exp (t * ε))⁻¹ * μ[fun ω => exp (t * X ω)] := by
+      have : exp (t * ε) * (μ {ω | exp (t * ε) ≤ exp (t * X ω)}).toReal ≤
+          μ[fun ω => exp (t * X ω)] :=
+        mul_meas_ge_le_integral_of_nonneg (fun x => (exp_pos _).le) h_int _
+      rwa [mul_comm (exp (t * ε))⁻¹, ← div_eq_mul_inv, le_div_iff' (exp_pos _)]
+    _ = exp (-t * ε) * mgf X μ t := by rw [neg_mul, exp_neg]; rfl
+#align probability_theory.measure_ge_le_exp_mul_mgf ProbabilityTheory.measure_ge_le_exp_mul_mgf
+
+/-- **Chernoff bound** on the lower tail of a real random variable. -/
+theorem measure_le_le_exp_mul_mgf [IsFiniteMeasure μ] (ε : ℝ) (ht : t ≤ 0)
+    (h_int : Integrable (fun ω => exp (t * X ω)) μ) :
+    (μ {ω | X ω ≤ ε}).toReal ≤ exp (-t * ε) * mgf X μ t := by
+  rw [← neg_neg t, ← mgf_neg, neg_neg, ← neg_mul_neg (-t)]
+  refine' Eq.trans_le _ (measure_ge_le_exp_mul_mgf (-ε) (neg_nonneg.mpr ht) _)
+  · congr with ω
+    simp only [Pi.neg_apply, neg_le_neg_iff]
+  · simp_rw [Pi.neg_apply, neg_mul_neg]
+    exact h_int
+#align probability_theory.measure_le_le_exp_mul_mgf ProbabilityTheory.measure_le_le_exp_mul_mgf
+
+/-- **Chernoff bound** on the upper tail of a real random variable. -/
+theorem measure_ge_le_exp_cgf [IsFiniteMeasure μ] (ε : ℝ) (ht : 0 ≤ t)
+    (h_int : Integrable (fun ω => exp (t * X ω)) μ) :
+    (μ {ω | ε ≤ X ω}).toReal ≤ exp (-t * ε + cgf X μ t) := by
+  refine' (measure_ge_le_exp_mul_mgf ε ht h_int).trans _
+  rw [exp_add]
+  exact mul_le_mul le_rfl (le_exp_log _) mgf_nonneg (exp_pos _).le
+#align probability_theory.measure_ge_le_exp_cgf ProbabilityTheory.measure_ge_le_exp_cgf
+
+/-- **Chernoff bound** on the lower tail of a real random variable. -/
+theorem measure_le_le_exp_cgf [IsFiniteMeasure μ] (ε : ℝ) (ht : t ≤ 0)
+    (h_int : Integrable (fun ω => exp (t * X ω)) μ) :
+    (μ {ω | X ω ≤ ε}).toReal ≤ exp (-t * ε + cgf X μ t) := by
+  refine' (measure_le_le_exp_mul_mgf ε ht h_int).trans _
+  rw [exp_add]
+  exact mul_le_mul le_rfl (le_exp_log _) mgf_nonneg (exp_pos _).le
+#align probability_theory.measure_le_le_exp_cgf ProbabilityTheory.measure_le_le_exp_cgf
+
+end MomentGeneratingFunction
+
+end ProbabilityTheory