mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: γ : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
inst✝² : PseudoEMetricSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
F : Filter γ
inst✝ : F.IsCountablyGenerated
μs : γ → ProbabilityMeasure Ω
μ : ProbabilityMeasure Ω
hne : F.NeBot
h :
  ∀ (f : Ω → ℝ),
    (∃ C, ∀ (x y : Ω), dist (f x) (f y) ≤ C) →
      (∃ L, LipschitzWith L f) → Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))
s : Set Ω
hs : IsClosed[PseudoEMetricSpace.toUniformSpace.toTopologicalSpace] s
ε : ℝ
ε_pos : 0 < ε
fs : ℕ → Ω → ℝ := fun n ω ↦ ↑((thickenedIndicator ⋯ s) ω)
key₁ : Tendsto (fun n ↦ ∫ (ω : Ω), fs n ω ∂↑μ) atTop (𝓝 ((↑μ).real s))
room₁ : (↑μ).real s < (↑μ).real s + ε / 2
M : ℕ
hM : ∀ (b : ℕ), M ≤ b → ∫ (ω : Ω), fs b ω ∂↑μ < (↑μ).real s + ε / 2
key₂ : Tendsto (fun i ↦ ∫ (ω : Ω), fs M ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), fs M ω ∂↑μ))
room₂ : ∫ (a : Ω), fs M a ∂↑μ < ∫ (a : Ω), fs M a ∂↑μ + ε / 2
x : γ
hx : ∫ (ω : Ω), fs M ω ∂↑(μs x) ≤ ∫ (a : Ω), fs M a ∂↑μ + ε / 2
⊢ (↑(μs x)).real s ≤ ∫ (ω : Ω), fs M ω ∂↑(μs x)

Tactic:
rw [← integral_indicator_one hs.measurableSet]

Goal state after:
γ : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
inst✝² : PseudoEMetricSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
F : Filter γ
inst✝ : F.IsCountablyGenerated
μs : γ → ProbabilityMeasure Ω
μ : ProbabilityMeasure Ω
hne : F.NeBot
h :
  ∀ (f : Ω → ℝ),
    (∃ C, ∀ (x y : Ω), dist (f x) (f y) ≤ C) →
      (∃ L, LipschitzWith L f) → Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))
s : Set Ω
hs : IsClosed[PseudoEMetricSpace.toUniformSpace.toTopologicalSpace] s
ε : ℝ
ε_pos : 0 < ε
fs : ℕ → Ω → ℝ := fun n ω ↦ ↑((thickenedIndicator ⋯ s) ω)
key₁ : Tendsto (fun n ↦ ∫ (ω : Ω), fs n ω ∂↑μ) atTop (𝓝 ((↑μ).real s))
room₁ : (↑μ).real s < (↑μ).real s + ε / 2
M : ℕ
hM : ∀ (b : ℕ), M ≤ b → ∫ (ω : Ω), fs b ω ∂↑μ < (↑μ).real s + ε / 2
key₂ : Tendsto (fun i ↦ ∫ (ω : Ω), fs M ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), fs M ω ∂↑μ))
room₂ : ∫ (a : Ω), fs M a ∂↑μ < ∫ (a : Ω), fs M a ∂↑μ + ε / 2
x : γ
hx : ∫ (ω : Ω), fs M ω ∂↑(μs x) ≤ ∫ (a : Ω), fs M a ∂↑μ + ε / 2
⊢ ∫ (x : Ω), s.indicator 1 x ∂↑(μs x) ≤ ∫ (ω : Ω), fs M ω ∂↑(μs x)
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: case inr
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
π : ι → Set (Set Ω)
a : ι
S : Finset ι
hp_ind : iIndepSets π κ μ
haS : a ∉ S
t1 t2 : Set Ω
s : Finset ι
hs_mem : s ⊆ S
ft1 : ι → Set Ω
hft1_mem : ∀ x ∈ s, ft1 x ∈ π x
ht1_eq : t1 = ⋂ x ∈ s, ft1 x
ht2_mem_pia : t2 ∈ π a
f : ι → Set Ω := fun n ↦ if n = a then t2 else if n ∈ s then ft1 n else univ
n : ι
hn_mem_insert : n ∈ insert a s
hn_mem : n ∈ s
⊢ (if n = a then t2 else if n ∈ s then ft1 n else univ) ∈ π n

Tactic:
· grind

Goal state after:
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
π : ι → Set (Set Ω)
S : Finset ι
hp_ind : iIndepSets π κ μ
t1 t2 : Set Ω
s : Finset ι
hs_mem : s ⊆ S
ft1 : ι → Set Ω
hft1_mem : ∀ x ∈ s, ft1 x ∈ π x
ht1_eq : t1 = ⋂ x ∈ s, ft1 x
n : ι
hnS : n ∈ s
haS : n ∉ S
ht2_mem_pia : t2 ∈ π n
f : ι → Set Ω := fun n_1 ↦ if n_1 = n then t2 else if n_1 ∈ s then ft1 n_1 else univ
h_f_mem : ∀ n_1 ∈ insert n s, f n_1 ∈ π n_1
h_f_mem_pi : ∀ n ∈ s, f n ∈ π n
⊢ False

Tactic:
exact haS (hs_mem hnS)

Goal state after:
Module: Mathlib.Probability.Density

Goal state before:
pp: Ω : Type u_1
G : Type u_2
mΩ : MeasurableSpace Ω
ℙ : Measure Ω
inst✝⁶ : Group G
mG : MeasurableSpace G
inst✝⁵ : MeasurableMul₂ G
inst✝⁴ : MeasurableInv G
μ : Measure G
inst✝³ : μ.IsMulLeftInvariant
X Y : Ω → G
inst✝² : SFinite μ
inst✝¹ : HasPDF X ℙ μ
inst✝ : HasPDF Y ℙ μ
σX : SigmaFinite (map X ℙ)
σY : SigmaFinite (map Y ℙ)
hXY : X ⟂ᵢ[ℙ] Y
this✝ : AEMeasurable X ℙ
this : AEMeasurable Y ℙ
⊢ (map X ℙ ∗ₘ map Y ℙ).HaveLebesgueDecomposition μ

Tactic:
apply HaveLebesgueDecomposition.mconv <;> exact HasPDF.absolutelyContinuous

Goal state after:
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: Ω : Type u_1
inst✝² : MeasurableSpace Ω
inst✝¹ : TopologicalSpace Ω
inst✝ : SecondCountableTopology Ω
S : Set (Set Ω)
ν : ProbabilityMeasure Ω
h : ∀ (u : Set Ω), IsOpen[inst✝¹] u → ∀ x ∈ u, ∃ s ∈ S, s ∈ 𝓝 x ∧ s ⊆ u
G : Set Ω
hG : IsOpen[inst✝¹] G
r : ℝ≥0
hr : r < ν G
s : ↑G → Set Ω
hsS : ∀ (x : ↑G), s x ∈ S
hs_nhds : ∀ (x : ↑G), s x ∈ 𝓝 ↑x
hsG : ∀ (x : ↑G), s x ⊆ G
T₀ : Set ↑G
T₀_count : T₀.Countable
hT₀ : ⋃ i ∈ T₀, interior (s i) = ⋃ i, interior (s i)
this : G ⊆ ⋃ i, interior (s i)
⊢ ⋃ i ∈ T₀, interior (s i) ⊆ ⋃ y ∈ T₀, s y

Tactic:
exact iUnion₂_mono fun i j ↦ interior_subset

Goal state after:
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : SecondCountableTopology Ω
inst✝¹ : OpensMeasurableSpace Ω
S : Set (Set Ω)
hS : IsPiSystem S
μ : ι → ProbabilityMeasure Ω
ν : ProbabilityMeasure Ω
l : Filter ι
inst✝ : l.IsCountablyGenerated
hmeas : ∀ s ∈ S, MeasurableSet s
h : ∀ (u : Set Ω), IsOpen[inst✝³] u → ∀ x ∈ u, ∃ s ∈ S, s ∈ 𝓝 x ∧ s ⊆ u
h' : ∀ s ∈ S, Tendsto (fun i ↦ (μ i) s) l (𝓝 (ν s))
⊢ Tendsto μ l (𝓝 ν)

Tactic:
by
  /- We apply the portmanteau theorem: it suffices to show that, given an open set `G`
  and `r < ν G`, then for large `i` one has `r < μᵢ G`. For this, we approximate `G` from inside by
  a finite union `G'` of elements of `S`, still with measure `> r`, by Lemma
  `ProbabilityMeasure.exists_lt_measure_biUnion_of_isOpen`. If we have `μᵢ G' → ν G'`,
  then we deduce `r < μᵢ G'` for large `i`, and therefore `r < μᵢ G`.

  Our assumption does not give directly `μᵢ G' → ν G'`, as `G'` does not belong to the π-system `S`.
  However, the inclusion-exclusion formula makes it possible to express `μᵢ G'` and `ν G'` in terms
  of the measures of intersections of elements of `S`, for which we have the convergence. It follows
  that `μᵢ G' → ν G'` holds, concluding the proof. This second step is already formalized in the
  lemma `IsPiSystem.tendsto_probabilityMeasure_biUnion`. -/
  rcases l.eq_or_neBot with rfl | hl
  · simp
  apply tendsto_of_forall_isOpen_le_liminf
  intro G hG
  refine (le_liminf_iff (isCoboundedUnder_ge_of_le (x := 1) l (by simp)) (by isBoundedDefault)).2
    (fun r hr ↦ ?_)
  obtain ⟨T, TS, T_meas, TG⟩ :
      ∃ T : Finset (Set Ω), (∀ t ∈ T, t ∈ S) ∧ (r < ν (⋃ t ∈ T, t)) ∧ (⋃ t ∈ T, t) ⊆ G :=
    ν.exists_lt_measure_biUnion_of_isOpen h hG hr
  have : Tendsto (fun i ↦ μ i (⋃ t ∈ T, t)) l (𝓝 (ν (⋃ t ∈ T, t))) :=
    hS.tendsto_probabilityMeasure_biUnion TS hmeas h'
  filter_upwards [(tendsto_order.1 this).1 r T_meas] with i hi
  exact hi.trans_le <| ProbabilityMeasure.apply_mono _ TG

Goal state after:
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
⊢ eVar[X; μ] < ∞

Tactic:
have := ENNReal.pow_lt_top (hX.sub <| memLp_const <| μ[X]).2 (n := 2)

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
this : eLpNorm (X - fun x ↦ ∫ (x : Ω), X x ∂μ) 2 μ ^ 2 < ∞
⊢ eVar[X; μ] < ∞
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hY : Integrable Y μ
c : ℝ
⊢ cov[X, fun ω ↦ Y ω + c; μ] = cov[X, Y; μ]

Tactic:
covariance_comm,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hY : Integrable Y μ
c : ℝ
⊢ cov[fun ω ↦ Y ω + c, X; μ] = cov[X, Y; μ]
Module: Mathlib.Probability.Independence.Integration

Goal state before:
pp: case cons.h_meas_f
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_3
X : ι → Ω → ℝ≥0∞
hX : iIndepFun X μ
x_mea : ∀ (i : ι), Measurable (X i)
this : IsProbabilityMeasure μ
j : ι
s : Finset ι
hj : j ∉ s
ihs : ∫⁻ (ω : Ω), ∏ i ∈ s, X i ω ∂μ = ∏ i ∈ s, ∫⁻ (ω : Ω), X i ω ∂μ
⊢ AEMeasurable (X j) μ

Tactic:
exact (x_mea j).aemeasurable

Goal state after:
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y Z : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
hZ : MemLp Z 2 μ
⊢ cov[X, Y + Z; μ] = cov[X, Y; μ] + cov[X, Z; μ]

Tactic:
covariance_comm,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y Z : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
hZ : MemLp Z 2 μ
⊢ cov[Y + Z, X; μ] = cov[X, Y; μ] + cov[X, Z; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[X, c • Y; μ] = c * cov[X, Y; μ]

Tactic:
covariance_comm,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[c • Y, X; μ] = c * cov[X, Y; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y Z T : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
hZ : MemLp Z 2 μ
hT : MemLp T 2 μ
⊢ cov[X, Z; μ] - cov[X, T; μ] - cov[Y, Z - T; μ] = cov[X, Z; μ] - cov[X, T; μ] - cov[Y, Z; μ] + cov[Y, T; μ]

Tactic:
covariance_sub_right hY hZ hT

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y Z T : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
hZ : MemLp Z 2 μ
hT : MemLp T 2 μ
⊢ cov[X, Z; μ] - cov[X, T; μ] - (cov[Y, Z; μ] - cov[Y, T; μ]) =
    cov[X, Z; μ] - cov[X, T; μ] - cov[Y, Z; μ] + cov[Y, T; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: case insert
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ cov[∑ i ∈ insert i s, X i, Y; μ] = ∑ i ∈ insert i s, cov[X i, Y; μ]

Tactic:
rw [Finset.sum_insert hi, Finset.sum_insert hi, covariance_add_left, h_ind]

Goal state after:
case insert
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ ∀ i ∈ s, MemLp (X i) 2 μ

case insert.hX
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ MemLp (X i) 2 μ

case insert.hY
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ MemLp (∑ x ∈ s, X x) 2 μ

case insert.hZ
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ MemLp Y 2 μ
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: case insert.hZ
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ MemLp Y 2 μ

Tactic:
· exact hY

Goal state after:
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s : Finset ι
inst✝ : IsFiniteMeasure μ
hX : ∀ i ∈ s, MemLp (X i) 2 μ
hY : MemLp Y 2 μ
⊢ cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]

Tactic:
induction s using Finset.induction with
  | empty => simp
  | insert i s hi h_ind =>
    rw [Finset.sum_insert hi, Finset.sum_insert hi, covariance_add_left, h_ind]
    · exact fun j hj ↦ hX j (by simp [hj])
    · exact hX i (by simp)
    · exact memLp_finsetSum' s (fun j hj ↦ hX j (by simp [hj]))
    · exact hY

Goal state after:
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s : Finset ι
inst✝ : IsFiniteMeasure μ
hX : ∀ i ∈ s, MemLp (X i) 2 μ
hY : MemLp Y 2 μ
⊢ cov[Y, ∑ i ∈ s, X i; μ] = ∑ i ∈ s, cov[Y, X i; μ]

Tactic:
covariance_comm,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s : Finset ι
inst✝ : IsFiniteMeasure μ
hX : ∀ i ∈ s, MemLp (X i) 2 μ
hY : MemLp Y 2 μ
⊢ cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[Y, X i; μ]
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_3
X : ι → Ω → ℝ
s : Finset ι
hs : ∀ i ∈ s, MemLp (X i) 2 μ
h : (↑s).Pairwise fun i j ↦ X i ⟂ᵢ[μ] X j
h'' : ∀ i ∈ s, X i =ᵐ[μ] 0
⊢ Var[∑ i ∈ s, X i; μ] = ∑ i ∈ s, Var[X i; μ]

Tactic:
variance_congr (Y := 0),

Goal state after:
case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_3
X : ι → Ω → ℝ
s : Finset ι
hs : ∀ i ∈ s, MemLp (X i) 2 μ
h : (↑s).Pairwise fun i j ↦ X i ⟂ᵢ[μ] X j
h'' : ∀ i ∈ s, X i =ᵐ[μ] 0
⊢ Var[0; μ] = ∑ i ∈ s, Var[X i; μ]

case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_3
X : ι → Ω → ℝ
s : Finset ι
hs : ∀ i ∈ s, MemLp (X i) 2 μ
h : (↑s).Pairwise fun i j ↦ X i ⟂ᵢ[μ] X j
h'' : ∀ i ∈ s, X i =ᵐ[μ] 0
⊢ ∑ i ∈ s, X i =ᵐ[μ] 0
Module: Mathlib.Probability.HasLaw

Goal state before:
pp: Ω : Type u_1
𝓧 : Type u_2
mΩ : MeasurableSpace Ω
m𝓧 : MeasurableSpace 𝓧
X : Ω → 𝓧
μ : Measure 𝓧
P : Measure Ω
Ω' : Type u_3
𝓨 : Type u_4
m' : MeasurableSpace Ω'
m𝓨 : MeasurableSpace 𝓨
P' : Measure Ω'
ν : Measure 𝓨
f : 𝓧 → 𝓨
Y : Ω' → 𝓧
hf : AEMeasurable f μ
hX : HasLaw X μ P
hY : HasLaw Y μ P'
h : HasLaw (fun ω ↦ f (X ω)) ν P
⊢ map f (map Y P') = ν

Tactic:
hY.map_eq,

Goal state after:
Ω : Type u_1
𝓧 : Type u_2
mΩ : MeasurableSpace Ω
m𝓧 : MeasurableSpace 𝓧
X : Ω → 𝓧
μ : Measure 𝓧
P : Measure Ω
Ω' : Type u_3
𝓨 : Type u_4
m' : MeasurableSpace Ω'
m𝓨 : MeasurableSpace 𝓨
P' : Measure Ω'
ν : Measure 𝓨
f : 𝓧 → 𝓨
Y : Ω' → 𝓧
hf : AEMeasurable f μ
hX : HasLaw X μ P
hY : HasLaw Y μ P'
h : HasLaw (fun ω ↦ f (X ω)) ν P
⊢ map f μ = ν
Module: Mathlib.MeasureTheory.Function.Floor

Goal state before:
pp: R : Type u_2
inst✝⁷ : Ring R
inst✝⁶ : LinearOrder R
inst✝⁵ : FloorRing R
inst✝⁴ : TopologicalSpace R
inst✝³ : OrderTopology R
inst✝² : MeasurableSpace R
inst✝¹ : IsStrictOrderedRing R
inst✝ : BorelSpace R
s : Set R
hs : MeasurableSet s
⊢ MeasurableSet (Int.fract '' s)

Tactic:
simp only [Int.image_fract, sub_eq_add_neg, image_add_right']

Goal state after:
R : Type u_2
inst✝⁷ : Ring R
inst✝⁶ : LinearOrder R
inst✝⁵ : FloorRing R
inst✝⁴ : TopologicalSpace R
inst✝³ : OrderTopology R
inst✝² : MeasurableSpace R
inst✝¹ : IsStrictOrderedRing R
inst✝ : BorelSpace R
s : Set R
hs : MeasurableSet s
⊢ MeasurableSet (⋃ m, (fun x ↦ x + ↑m) ⁻¹' s ∩ Ico 0 1)
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: case refine_2.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : ((s n).indicator fun x ↦ 1) ⁻¹' u = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = Set.univ
⊢ MeasurableSet t

Tactic:
rw [heq] at hu'

Goal state after:
case refine_2.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : Set.univ = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = Set.univ
⊢ MeasurableSet t
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: case refine_2.inr.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : ((s n).indicator fun x ↦ 1) ⁻¹' u = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = s n
⊢ MeasurableSet t

Tactic:
rw [heq] at hu'

Goal state after:
case refine_2.inr.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : s n = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = s n
⊢ MeasurableSet t
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: case refine_2.inr.inr.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : ((s n).indicator fun x ↦ 1) ⁻¹' u = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = (s n)ᶜ
⊢ MeasurableSet t

Tactic:
rw [heq] at hu'

Goal state after:
case refine_2.inr.inr.inl
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : (s n)ᶜ = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = (s n)ᶜ
⊢ MeasurableSet t
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: case refine_2.inr.inr.inr
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : ((s n).indicator fun x ↦ 1) ⁻¹' u = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = ∅
⊢ MeasurableSet t

Tactic:
rw [heq] at hu'

Goal state after:
case refine_2.inr.inr.inr
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
β : ι → Type u_3
inst✝⁵ : (i : ι) → TopologicalSpace (β i)
inst✝⁴ : ∀ (i : ι), MetrizableSpace (β i)
mβ : (i : ι) → MeasurableSpace (β i)
inst✝³ : ∀ (i : ι), BorelSpace (β i)
inst✝² : Preorder ι
inst✝¹ : (i : ι) → MulZeroOneClass (β i)
inst✝ : ∀ (i : ι), Nontrivial (β i)
s : ι → Set Ω
hsm : ∀ (i : ι), MeasurableSet (s i)
i : ι
t✝ : Set Ω
n : ι
ht : MeasurableSet t✝
t : Set Ω
hn : n ≤ i
u : Set (β n)
left✝ : MeasurableSet u
hu' : ∅ = t
heq : ((s n).indicator fun x ↦ 1) ⁻¹' u = ∅
⊢ MeasurableSet t
Module: Mathlib.MeasureTheory.Function.SpecialFunctions.Sinc

Goal state before:
pp: μ : Measure ℝ
inst✝ : IsFiniteMeasure μ
⊢ Integrable sinc μ

Tactic:
refine Integrable.mono' (g := fun _ ↦ 1) (by fun_prop) (by fun_prop) <| ae_of_all _ fun x ↦ ?_
  rw [Real.norm_eq_abs]
  exact abs_sinc_le_one x

Goal state after:
Module: Mathlib.MeasureTheory.Function.Piecewise

Goal state before:
pp: ι : Type u_1
α : Type u_2
β : Type u_3
inst✝² : MeasurableSpace α
s : ι → Set α
f : ι → α → β
μ : Measure α
inst✝¹ : Countable ι
hs : IndexedPartition s
hm : ∀ (i : ι), MeasurableSet (s i)
inst✝ : TopologicalSpace β
hf : ∀ (i : ι), AEStronglyMeasurable (f i) μ
⊢ AEStronglyMeasurable (hs.piecewise f) μ

Tactic:
choose p hp hq using hf
  refine ⟨hs.piecewise p, hs.stronglyMeasurable_piecewise hm hp, ?_⟩
  filter_upwards [ae_all_iff.2 hq] with x hx using hx (hs.index x)

Goal state after:
Module: Mathlib.MeasureTheory.Function.UnifTight

Goal state before:
pp: α : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace α
μ : Measure α
inst✝ : NormedAddCommGroup β
f g : ι → α → β
p : ℝ≥0∞
hf : UnifTight f p μ
hg : UnifTight g p μ
hf_meas : ∀ (i : ι), AEStronglyMeasurable (f i) μ
hg_meas : ∀ (i : ι), AEStronglyMeasurable (g i) μ
ε : ℝ≥0
hε : 0 < ε
⊢ ∃ s, μ s ≠ ∞ ∧ ∀ (i : ι), eLpNorm (sᶜ.indicator ((f + g) i)) p μ ≤ ↑ε

Tactic:
rcases exists_Lp_half β μ p (coe_ne_zero.mpr hε.ne') with ⟨η, hη_pos, hη⟩

Goal state after:
α : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace α
μ : Measure α
inst✝ : NormedAddCommGroup β
f g : ι → α → β
p : ℝ≥0∞
hf : UnifTight f p μ
hg : UnifTight g p μ
hf_meas : ∀ (i : ι), AEStronglyMeasurable (f i) μ
hg_meas : ∀ (i : ι), AEStronglyMeasurable (g i) μ
ε : ℝ≥0
hε : 0 < ε
η : ℝ≥0∞
hη_pos : 0 < η
hη :
  ∀ (f g : α → β),
    AEStronglyMeasurable f μ →
      AEStronglyMeasurable g μ → eLpNorm f p μ ≤ η → eLpNorm g p μ ≤ η → eLpNorm (f + g) p μ < ↑ε
⊢ ∃ s, μ s ≠ ∞ ∧ ∀ (i : ι), eLpNorm (sᶜ.indicator ((f + g) i)) p μ ≤ ↑ε