mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.MeasureTheory.VectorMeasure.Decomposition.Lebesgue

Goal state before:
pp: α : Type u_1
m : MeasurableSpace α
μ : Measure α
s t : SignedMeasure α
f : α → ℝ
hf : Measurable f
hfi : Integrable f μ
htμ : t ⟂ᵥ μ.toENNRealVectorMeasure
hadd : s = t + μ.withDensityᵥ f
this✝ : IsFiniteMeasure (μ.withDensity fun x ↦ ENNReal.ofReal (f x))
this : IsFiniteMeasure (μ.withDensity fun x ↦ ENNReal.ofReal ((-f) x))
i : Set α
hi : MeasurableSet i
⊢ (t + μ.withDensityᵥ f) i =
    ((t.toJordanDecomposition.posPart + μ.withDensity fun x ↦ ENNReal.ofReal (f x)).toSignedMeasure -
        (t.toJordanDecomposition.negPart + μ.withDensity fun x ↦ ENNReal.ofReal (-f x)).toSignedMeasure)
      i

Tactic:
rw [_root_.sub_apply, toSignedMeasure_apply_measurable hi,
      toSignedMeasure_apply_measurable hi, measureReal_add_apply, measureReal_add_apply,
      add_sub_add_comm, ← toSignedMeasure_apply_measurable hi,
      ← toSignedMeasure_apply_measurable hi, ← _root_.sub_apply,
      ← JordanDecomposition.toSignedMeasure, toSignedMeasure_toJordanDecomposition,
      _root_.add_apply, ← toSignedMeasure_apply_measurable hi,
      ← toSignedMeasure_apply_measurable hi,
      withDensityᵥ_eq_withDensity_pos_part_sub_withDensity_neg_part hfi,
      _root_.sub_apply]

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

Goal state before:
pp: Ω : Type u_1
inst✝⁴ : TopologicalSpace Ω
inst✝³ : MeasurableSpace Ω
inst✝² : OpensMeasurableSpace Ω
ι : Type u_2
L : Filter ι
inst✝¹ : L.IsCountablyGenerated
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
fs : ι → Ω →ᵇ ℝ≥0
c : ℝ≥0
fs_le_const : ∀ᶠ (i : ι) in L, ∀ᵐ (ω : Ω) ∂μ, (fs i) ω ≤ c
f : Ω → ℝ≥0
fs_lim : ∀ᵐ (ω : Ω) ∂μ, Tendsto (fun i ↦ (fs i) ω) L (𝓝 (f ω))
⊢ Tendsto (fun i ↦ ∫⁻ (ω : Ω), ↑((fs i) ω) ∂μ) L (𝓝 (∫⁻ (ω : Ω), ↑(f ω) ∂μ))

Tactic:
by
  refine tendsto_lintegral_filter_of_dominated_convergence (fun _ ↦ c)
    (Eventually.of_forall fun i ↦ (ENNReal.continuous_coe.comp (fs i).continuous).measurable) ?_
    (@lintegral_const_lt_top _ _ μ _ _ (@ENNReal.coe_ne_top c)).ne ?_
  · simpa only [Function.comp_apply, ENNReal.coe_le_coe] using fs_le_const
  · simpa only [Function.comp_apply, ENNReal.tendsto_coe] using fs_lim

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

Goal state before:
pp: Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : HasOuterApproxClosed Ω
inst✝¹ : BorelSpace Ω
μ ν : Measure Ω
inst✝ : IsFiniteMeasure μ
h : ∀ (f : Ω →ᵇ ℝ≥0), ∫⁻ (x : Ω), ↑(f x) ∂μ = ∫⁻ (x : Ω), ↑(f x) ∂ν
key : ∀ {F : Set Ω}, IsClosed[inst✝³] F → μ F = ν F
⊢ borel Ω = MeasurableSpace.generateFrom {s | IsClosed[inst✝³] s}

Tactic:
borel_eq_generateFrom_isClosed

Goal state after:
Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : HasOuterApproxClosed Ω
inst✝¹ : BorelSpace Ω
μ ν : Measure Ω
inst✝ : IsFiniteMeasure μ
h : ∀ (f : Ω →ᵇ ℝ≥0), ∫⁻ (x : Ω), ↑(f x) ∂μ = ∫⁻ (x : Ω), ↑(f x) ∂ν
key : ∀ {F : Set Ω}, IsClosed[inst✝³] F → μ F = ν F
⊢ MeasurableSpace.generateFrom {s | IsClosed[inst✝³] s} = MeasurableSpace.generateFrom {s | IsClosed[inst✝³] s}
Module: Mathlib.MeasureTheory.Function.ConditionalExpectation.Real

Goal state before:
pp: case pos
α : Type u_1
m m0 : MeasurableSpace α
μ : Measure α
E : Type u_2
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
inst✝ : CompleteSpace E
f : α → E
p : ℝ≥0∞
hp : 1 ≤ p
hf : MemLp f p μ
⊢ ENNReal.ofReal (lpNorm μ[f | m] p μ) ≤ ENNReal.ofReal (lpNorm f p μ)

Tactic:
exact ENNReal.ofReal_le_ofReal (hf.lpNorm_condExp_le_lpNorm hp)

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

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
p : ℝ≥0∞
f : ι → α → β
hp : p ≠ 0
hp' : p ≠ ∞
hf : ∀ (i : ι), StronglyMeasurable (f i)
ε : ℝ
hε : 0 < ε
hfu : UnifIntegrable f p μ
M : ℝ≥0
hM : ∀ (i : ι), eLpNorm (f i) p μ ≤ ↑M
ℐ : ℝ≥0 → ι
δ : ℝ≥0
hδpos : 0 < ↑δ
hδ :
  ∀ (i : ι) (s : Set α), MeasurableSet s → μ s ≤ ENNReal.ofReal ↑δ → eLpNorm (s.indicator (f i)) p μ ≤ ENNReal.ofReal ε
hℐ : ∀ (C : ℝ≥0), ENNReal.ofReal ↑δ < μ {x | C ≤ ‖f (ℐ C) x‖₊}
this : ↑2 * ↑(max M 1) ≤ eLpNorm (f (ℐ (2 * max M 1 * (δ ^ (1 / p.toReal))⁻¹))) p μ
⊢ False

Tactic:
← NNReal.inv_rpow

Goal state after:
α : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace α
μ : Measure α
inst✝ : NormedAddCommGroup β
p : ℝ≥0∞
f : ι → α → β
hp : p ≠ 0
hp' : p ≠ ∞
hf : ∀ (i : ι), StronglyMeasurable (f i)
ε : ℝ
hε : 0 < ε
hfu : UnifIntegrable f p μ
M : ℝ≥0
hM : ∀ (i : ι), eLpNorm (f i) p μ ≤ ↑M
ℐ : ℝ≥0 → ι
δ : ℝ≥0
hδpos : 0 < ↑δ
hδ :
  ∀ (i : ι) (s : Set α), MeasurableSet s → μ s ≤ ENNReal.ofReal ↑δ → eLpNorm (s.indicator (f i)) p μ ≤ ENNReal.ofReal ε
hℐ : ∀ (C : ℝ≥0), ENNReal.ofReal ↑δ < μ {x | C ≤ ‖f (ℐ C) x‖₊}
this : ↑2 * ↑(max M 1) ≤ eLpNorm (f (ℐ (2 * max M 1 * δ⁻¹ ^ (1 / p.toReal)))) p μ
⊢ False
Module: Mathlib.MeasureTheory.Measure.FiniteMeasure

Goal state before:
pp: case pos
Ω : Type u_1
Ω' : Type u_2
inst✝¹ : MeasurableSpace Ω
inst✝ : MeasurableSpace Ω'
f : Ω → Ω'
μ : FiniteMeasure Ω
hf : AEMeasurable f ↑μ
⊢ (Measure.map f ↑μ) univ ≤ ↑μ univ

Tactic:
rw [Measure.map_apply_of_aemeasurable hf MeasurableSet.univ]

Goal state after:
case pos
Ω : Type u_1
Ω' : Type u_2
inst✝¹ : MeasurableSpace Ω
inst✝ : MeasurableSpace Ω'
f : Ω → Ω'
μ : FiniteMeasure Ω
hf : AEMeasurable f ↑μ
⊢ ↑μ (f ⁻¹' univ) ≤ ↑μ univ
Module: Mathlib.MeasureTheory.Measure.FiniteMeasure

Goal state before:
pp: Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : FiniteMeasure Ω
s : Set Ω
hs : MeasurableSet s
⊢ (Measure.comap f (Measure.map f ↑μ)) s = ↑μ s

Tactic:
Measure.comap_apply,

Goal state after:
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : FiniteMeasure Ω
s : Set Ω
hs : MeasurableSet s
⊢ (Measure.map f ↑μ) (f '' s) = ↑μ s

case hfi
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : FiniteMeasure Ω
s : Set Ω
hs : MeasurableSet s
⊢ Function.Injective f

case hf
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : FiniteMeasure Ω
s : Set Ω
hs : MeasurableSet s
⊢ ∀ (s : Set Ω), MeasurableSet s → MeasurableSet (f '' s)

case hs
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : FiniteMeasure Ω
s : Set Ω
hs : MeasurableSet s
⊢ MeasurableSet s
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: case inr
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m' _mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
s t : Set Ω
a✝ : s ∈ {s | MeasurableSet s}
ht : t = ∅ ∨ t = univ
h : IsMarkovKernel κ
a : α
⊢ (κ a) (s ∩ t) = (κ a) s * (κ a) t

Tactic:
rcases ht with ht | ht

Goal state after:
case inr.inl
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m' _mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
s t : Set Ω
a✝ : s ∈ {s | MeasurableSet s}
h : IsMarkovKernel κ
a : α
ht : t = ∅
⊢ (κ a) (s ∩ t) = (κ a) s * (κ a) t

case inr.inr
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m' _mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
s t : Set Ω
a✝ : s ∈ {s | MeasurableSet s}
h : IsMarkovKernel κ
a : α
ht : t = univ
⊢ (κ a) (s ∩ t) = (κ a) s * (κ a) t
Module: Mathlib.Probability.Independence.Basic

Goal state before:
pp: case e'_3.e'_6
Ω : Type u_10
Ω' : Type u_11
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
μ : Measure Ω
ν : Measure Ω'
inst✝³ : IsProbabilityMeasure μ
inst✝² : IsProbabilityMeasure ν
𝓧 : Type u_12
𝓨 : Type u_13
inst✝¹ : MeasurableSpace 𝓧
inst✝ : MeasurableSpace 𝓨
X : Ω → 𝓧
Y : Ω' → 𝓨
mX : Measurable X
mY : Measurable Y
⊢ Measure.map Y (Measure.map Prod.snd (μ.prod ν)) = Measure.map Y ν

Tactic:
Measure.map_snd_prod,

Goal state after:
case e'_3.e'_6
Ω : Type u_10
Ω' : Type u_11
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
μ : Measure Ω
ν : Measure Ω'
inst✝³ : IsProbabilityMeasure μ
inst✝² : IsProbabilityMeasure ν
𝓧 : Type u_12
𝓨 : Type u_13
inst✝¹ : MeasurableSpace 𝓧
inst✝ : MeasurableSpace 𝓨
X : Ω → 𝓧
Y : Ω' → 𝓨
mX : Measurable X
mY : Measurable Y
⊢ Measure.map Y (μ univ • ν) = Measure.map Y ν
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case mpr
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
inst✝ : IsZeroOrMarkovKernel κ
hf : Measurable f
hg : Measurable g
s : Set β
t : Set β'
h : IndepSet (f ⁻¹' s) (g ⁻¹' t) κ μ
hs : MeasurableSet s
ht : MeasurableSet t
⊢ ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)

Tactic:
rwa [← indepSet_iff_measure_inter_eq_mul (hf hs) (hg ht) κ μ]

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

Goal state before:
pp: case pos
α : Type u_1
Ω : Type u_2
β : Type u_4
γ : Type u_6
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteKernel κ
f : Ω → β
g : Ω → γ
hf : Measurable f
hg : Measurable g
h :
  ∀ (s : Set β) (t : Set γ),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
u : Set α
s : Set β
t : Set γ
hu : MeasurableSet u
hs : MeasurableSet s
ht : MeasurableSet t
h_set_eq : ∀ (ω : α), Prod.mk ω ⁻¹' u ×ˢ s ×ˢ t = if ω ∈ u then s ×ˢ t else ∅
ω : α
hω : (κ ω) (f ⁻¹' s ∩ g ⁻¹' t) = (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t)
hωu : ω ∈ u
⊢ (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t) = ((κ.map f ×ₖ κ.map g) ω) (s ×ˢ t)

Tactic:
prod_apply_prod,

Goal state after:
case pos
α : Type u_1
Ω : Type u_2
β : Type u_4
γ : Type u_6
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteKernel κ
f : Ω → β
g : Ω → γ
hf : Measurable f
hg : Measurable g
h :
  ∀ (s : Set β) (t : Set γ),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
u : Set α
s : Set β
t : Set γ
hu : MeasurableSet u
hs : MeasurableSet s
ht : MeasurableSet t
h_set_eq : ∀ (ω : α), Prod.mk ω ⁻¹' u ×ˢ s ×ˢ t = if ω ∈ u then s ×ˢ t else ∅
ω : α
hω : (κ ω) (f ⁻¹' s ∩ g ⁻¹' t) = (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t)
hωu : ω ∈ u
⊢ (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t) = ((κ.map f) ω) s * ((κ.map g) ω) t
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case e'_3
α : Type u_1
Ω : Type u_2
β : Type u_4
γ : Type u_6
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteKernel κ
f : Ω → β
g : Ω → γ
hf : Measurable f
hg : Measurable g
s : Set β
t : Set γ
hs : MeasurableSet s
ht : MeasurableSet t
u : Set α
h : ∫⁻ (a : α) in u, ((κ.map fun ω ↦ (f ω, g ω)) a) (s ×ˢ t) ∂μ = ∫⁻ (a : α) in u, ((κ.map f ×ₖ κ.map g) a) (s ×ˢ t) ∂μ
hu : MeasurableSet u
hμu : μ u < ⊤
ω : α
⊢ (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t) = ((κ.map f ×ₖ κ.map g) ω) (s ×ˢ t)

Tactic:
prod_apply_prod,

Goal state after:
case e'_3
α : Type u_1
Ω : Type u_2
β : Type u_4
γ : Type u_6
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteKernel κ
f : Ω → β
g : Ω → γ
hf : Measurable f
hg : Measurable g
s : Set β
t : Set γ
hs : MeasurableSet s
ht : MeasurableSet t
u : Set α
h : ∫⁻ (a : α) in u, ((κ.map fun ω ↦ (f ω, g ω)) a) (s ×ˢ t) ∂μ = ∫⁻ (a : α) in u, ((κ.map f ×ₖ κ.map g) a) (s ×ˢ t) ∂μ
hu : MeasurableSet u
hμu : μ u < ⊤
ω : α
⊢ (κ ω) (f ⁻¹' s) * (κ ω) (g ⁻¹' t) = ((κ.map f) ω) s * ((κ.map g) ω) t
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case refine_2
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
T : Set (Set Ω)
TS : T ⊆ S
T_count : T.Countable
hT : ⋃ t ∈ T, t = G
this✝ : T.Nonempty
f : ℕ → Set Ω
hf : T = range f
G_eq : G = ⋃ n, f n
this : Tendsto (fun i ↦ ν (accumulate f i)) atTop (𝓝 (ν G))
n : ℕ
hn : r < ν (accumulate f n)
⊢ ⋃ t ∈ Finset.image f (Finset.range (n + 1)), t ⊆ G

Tactic:
· simpa [G_eq] using fun i _ ↦ subset_iUnion f i

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
m : ι → MeasurableSpace Ω
h_le : ∀ (i : ι), m i ≤ _mΩ
π : ι → Set (Set Ω)
h_pi : ∀ (n : ι), IsPiSystem (π n)
h_generate : ∀ (i : ι), m i = generateFrom (π i)
h_ind : iIndepSets π κ μ
hμ : μ ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μ] ⇑η
hη : IsMarkovKernel η
s : Finset ι
f : ι → Set Ω
a : ι
S : Finset ι
ha_notin_S : a ∉ S
h_rec : (∀ i ∈ S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i) → ∀ᵐ (a : α) ∂μ, (η a) (⋂ i ∈ S, f i) = ∏ i ∈ S, (η a) (f i)
hf_m : ∀ i ∈ insert a S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i
hf_m_S : ∀ x ∈ S, MeasurableSet (f x)
p : Set (Set Ω) := piiUnionInter π ↑S
m_p : MeasurableSpace Ω := generateFrom p
hS_eq_generate : m_p = generateFrom p
h_indep : Indep m_p (m a) η μ
h : ∀ᵐ (a_1 : α) ∂μ, (η a_1) (f a ∩ ⋂ n ∈ S, f n) = (η a_1) (f a) * (η a_1) (⋂ n ∈ S, f n)
a' : α
ha' : (η a') (⋂ i ∈ S, f i) = ∏ i ∈ S, (η a') (f i)
h' : (η a') (f a ∩ ⋂ n ∈ S, f n) = (η a') (f a) * (η a') (⋂ n ∈ S, f n)
⊢ (η a') (⋂ i ∈ insert a S, f i) = ∏ i ∈ insert a S, (η a') (f i)

Tactic:
rwa [Finset.set_biInter_insert, Finset.prod_insert ha_notin_S, ← ha']

Goal state after:
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
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
⊢ cov[X, Y; μ] = ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ

Tactic:
covariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
⊢ ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) * (Y ω - ∫ (x : Ω), Y x ∂μ) ∂μ =
    ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ
Module: Mathlib.Probability.Independence.Integration

Goal state before:
pp: case indicator
Ω : Type u_1
f : Ω → ℝ≥0∞
Mf Mg mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
hMg : Mg ≤ mΩ
h_ind : Indep Mf Mg μ
h_meas_f : Measurable f
h_measM_f : Measurable f
c : ℝ≥0∞
s : Set Ω
h_s : MeasurableSet s
⊢ ∫⁻ (ω : Ω), f ω * s.indicator (fun x ↦ c) ω ∂μ = (∫⁻ (ω : Ω), f ω ∂μ) * ∫⁻ (ω : Ω), s.indicator (fun x ↦ c) ω ∂μ

Tactic:
apply lintegral_mul_indicator_eq_lintegral_mul_lintegral_indicator hMf _ (hMg _ h_s) _ h_meas_f

Goal state after:
Ω : Type u_1
f : Ω → ℝ≥0∞
Mf Mg mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
hMg : Mg ≤ mΩ
h_ind : Indep Mf Mg μ
h_meas_f : Measurable f
h_measM_f : Measurable f
c : ℝ≥0∞
s : Set Ω
h_s : MeasurableSet s
⊢ IndepSets {s | MeasurableSet s} {s} μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
hX : AEMeasurable X μ
⊢ cov[X, X; μ] = Var[X; μ]

Tactic:
covariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
hX : AEMeasurable X μ
⊢ ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) * (X ω - ∫ (x : Ω), X x ∂μ) ∂μ = Var[X; μ]
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, Z; μ] + cov[Y, Z; μ]

Tactic:
covariance,

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
⊢ ∫ (ω : Ω), ((X + Y) ω - ∫ (x : Ω), (X + Y) x ∂μ) * (Z ω - ∫ (x : Ω), Z x ∂μ) ∂μ =
    ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) * (Z ω - ∫ (x : Ω), Z x ∂μ) ∂μ +
      ∫ (ω : Ω), (Y ω - ∫ (x : Ω), Y x ∂μ) * (Z ω - ∫ (x : Ω), Z x ∂μ) ∂μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : AEStronglyMeasurable X μ
c : ℝ
⊢ Var[fun ω ↦ X ω + -c; μ] = Var[X; μ]

Tactic:
variance_add_const hX (-c)

Goal state after:
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[c • X, Y; μ] = c * cov[X, Y; μ]

Tactic:
covariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ ∫ (ω : Ω), ((c • X) ω - ∫ (x : Ω), (c • X) x ∂μ) * (Y ω - ∫ (x : Ω), Y x ∂μ) ∂μ =
    c * ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) * (Y ω - ∫ (x : Ω), Y x ∂μ) ∂μ
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
Ω' : Type u_2
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
X Y : Ω → ℝ
Z : Ω' ≃ᵐ Ω
⊢ cov[X, Y; Measure.map (⇑Z) μ] = cov[X ∘ ⇑Z, Y ∘ ⇑Z; μ]

Tactic:
covariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
Ω' : Type u_2
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
X Y : Ω → ℝ
Z : Ω' ≃ᵐ Ω
⊢ ∫ (ω : Ω),
      (X ω - ∫ (x : Ω), X x ∂Measure.map (⇑Z) μ) * (Y ω - ∫ (x : Ω), Y x ∂Measure.map (⇑Z) μ) ∂Measure.map (⇑Z) μ =
    ∫ (ω : Ω'), ((X ∘ ⇑Z) ω - ∫ (x : Ω'), (X ∘ ⇑Z) x ∂μ) * ((Y ∘ ⇑Z) ω - ∫ (x : Ω'), (Y ∘ ⇑Z) x ∂μ) ∂μ
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
Ω' : Type u_2
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
Z : Ω' → Ω
hX : AEStronglyMeasurable X (Measure.map Z μ)
hY : AEStronglyMeasurable Y (Measure.map Z μ)
hZ : AEMeasurable Z μ
⊢ cov[X, Y; Measure.map Z μ] = cov[X ∘ Z, Y ∘ Z; μ]

Tactic:
covariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
Ω' : Type u_2
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
Z : Ω' → Ω
hX : AEStronglyMeasurable X (Measure.map Z μ)
hY : AEStronglyMeasurable Y (Measure.map Z μ)
hZ : AEMeasurable Z μ
⊢ ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂Measure.map Z μ) * (Y ω - ∫ (x : Ω), Y x ∂Measure.map Z μ) ∂Measure.map Z μ =
    ∫ (ω : Ω'), ((X ∘ Z) ω - ∫ (x : Ω'), (X ∘ Z) x ∂μ) * ((Y ∘ Z) ω - ∫ (x : Ω'), (Y ∘ Z) x ∂μ) ∂μ
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
hX : HasLaw X μ P
p : 𝓧 → Prop
hp : MeasurableSet {x | p x}
⊢ P.real {ω | p (X ω)} = μ.real {x | p x}

Tactic:
by
  rw [← hX.map_eq, map_measureReal_apply_of_aemeasurable hX.aemeasurable hp]
  simp

Goal state after:
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 ∘ X) P = ν

Tactic:
Function.comp_def,

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 (fun x ↦ f (X x)) P = ν
Module: Mathlib.Probability.IdentDistrib

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
inst✝² : MeasurableSpace α
inst✝¹ : MeasurableSpace β
inst✝ : MeasurableSpace γ
μ : Measure α
ν : Measure β
f : α → γ
g : β → γ
h : IdentDistrib f g μ ν
p : γ → Prop
pmeas : MeasurableSet {x | p x}
hp : ∀ᵐ (x : α) ∂μ, p (f x)
⊢ ∀ᵐ (x : β) ∂ν, p (g x)

Tactic:
by
  apply (ae_map_iff h.aemeasurable_snd pmeas).1
  rw [← h.map_eq]
  exact (ae_map_iff h.aemeasurable_fst pmeas).2 hp

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

Goal state before:
pp: ι : Type u_1
E : Type u_2
Ω' : Type u_3
Ω'' : Type u_4
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝⁵ : IsProbabilityMeasure μ'
m'' : MeasurableSpace Ω''
μ'' : Measure Ω''
inst✝⁴ : IsProbabilityMeasure μ''
mE : MeasurableSpace E
l : Filter ι
inst✝³ : SeminormedAddCommGroup E
inst✝² : SecondCountableTopology E
inst✝¹ : BorelSpace E
X : ι → Ω'' → E
inst✝ : l.IsCountablyGenerated
Y : ι → Ω'' → E
Z : Ω' → E
hXZ : TendstoInDistribution X l Z (fun x ↦ μ'') μ'
hXY : TendstoInMeasure μ'' (Y - X) l 0
hY : ∀ (i : ι), AEMeasurable (Y i) μ''
hZ : AEMeasurable Z μ'
hX : ∀ (i : ι), AEMeasurable (X i) μ''
hE : Nonempty E
x₀ : E := hE.some
F : E → ℝ
M : ℝ
hF_bounded : ∀ (x y : E), dist (F x) (F y) ≤ M
L : NNReal
hF_lip : LipschitzWith L F
hF_cont : Continuous[PseudoMetricSpace.toUniformSpace.toTopologicalSpace, _] F
hL : 0 < L
ε : ℝ
hε : ε > 0
n : ι
h_int_Y : Integrable (fun x ↦ F (Y n x)) μ''
h_int_X : Integrable (fun x ↦ F (X n x)) μ''
⊢ Integrable (fun a ↦ F (Y n a) - F (X n a)) μ''

Tactic:
exact h_int_Y.sub h_int_X

Goal state after:
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 ⁻¹' s)

Tactic:
Int.preimage_fract

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: Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i : ι
m' : MeasurableSpace Ω
f : Filtration ι m
left✝ : f ∈ s
hfm' : ↑f i = m'
⊢ ↑f i ≤ m

Tactic:
exact f.le i

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 : MeasurableSpace β
inst✝ : Countable ι
hs : IndexedPartition s
hm : ∀ (i : ι), MeasurableSet (s i)
p : ι → α → β
hp : ∀ (i : ι), Measurable (p i)
hq : ∀ (i : ι), f i =ᵐ[μ] p i
⊢ hs.piecewise f =ᵐ[μ] hs.piecewise p

Tactic:
filter_upwards [ae_all_iff.2 hq] with x hx using hx (hs.index 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
p : ι → α → β
hp : ∀ (i : ι), StronglyMeasurable (p i)
hq : ∀ (i : ι), f i =ᵐ[μ] p i
⊢ hs.piecewise f =ᵐ[μ] hs.piecewise p

Tactic:
filter_upwards [ae_all_iff.2 hq] with x hx using hx (hs.index x)

Goal state after: