mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: case pos
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
hν : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
u : Set γ
hu : u ∈ countablePartition γ n
this✝ : IsFiniteKernel κ
hu_meas : MeasurableSet u
this :
  ∫⁻ (x : γ) in u, (κ a) (countablePartitionSet n x ×ˢ s) / (ν a) (countablePartitionSet n x) ∂ν a =
    ∫⁻ (x : γ) in u, (κ a) (u ×ˢ s) / (ν a) u ∂ν a
h0 : (ν a) u = 0
h0' : (κ a) {p | p.1 ∈ u} = 0
⊢ 0 = (κ a) (u ×ˢ s)

Tactic:
refine (measure_mono_null ?_ h0').symm

Goal state after:
case pos
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
hν : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
u : Set γ
hu : u ∈ countablePartition γ n
this✝ : IsFiniteKernel κ
hu_meas : MeasurableSet u
this :
  ∫⁻ (x : γ) in u, (κ a) (countablePartitionSet n x ×ˢ s) / (ν a) (countablePartitionSet n x) ∂ν a =
    ∫⁻ (x : γ) in u, (κ a) (u ×ˢ s) / (ν a) u ∂ν a
h0 : (ν a) u = 0
h0' : (κ a) {p | p.1 ∈ u} = 0
⊢ u ×ˢ s ⊆ {p | p.1 ∈ u}
Module: Mathlib.Probability.Kernel.Disintegration.StandardBorel

Goal state before:
pp: case neg
α : Type u_1
β : Type u_2
γ : Type u_3
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝³ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
η : Kernel α ℝ
inst✝ : IsMarkovKernel η
a : α
h : a ∉ (fun a ↦ (η a) (range (embeddingReal Ω))ᶜ) ⁻¹' {0}
⊢ ((deterministic (fun x ↦ Exists.choose ⋯) ⋯) a) (range (embeddingReal Ω)) = 1

Tactic:
rw [deterministic_apply]
    simp [(range_nonempty (embeddingReal Ω)).choose_spec]

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.Integral

Goal state before:
pp: α : Type u_1
Ω : Type u_2
E : Type u_3
mα : MeasurableSpace α
inst✝⁵ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
inst✝³ : Nonempty Ω
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
ρ : Measure (α × Ω)
inst✝ : IsFiniteMeasure ρ
f : α × Ω → E
hf_int : Integrable f ρ
x : α
⊢ ‖‖∫ (y : Ω), f (x, y) ∂ρ.condKernel x‖‖ ≤ ‖∫ (y : Ω), ‖f (x, y)‖ ∂ρ.condKernel x‖

Tactic:
norm_norm

Goal state after:
α : Type u_1
Ω : Type u_2
E : Type u_3
mα : MeasurableSpace α
inst✝⁵ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
inst✝³ : Nonempty Ω
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
ρ : Measure (α × Ω)
inst✝ : IsFiniteMeasure ρ
f : α × Ω → E
hf_int : Integrable f ρ
x : α
⊢ ‖∫ (y : Ω), f (x, y) ∂ρ.condKernel x‖ ≤ ‖∫ (y : Ω), ‖f (x, y)‖ ∂ρ.condKernel x‖
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
a : α
x : γ
s : Set β
⊢ κ.density ν a x s ≤ 1

Tactic:
refine limsup_le_of_le ?_ ?_

Goal state after:
case refine_1
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
a : α
x : γ
s : Set β
⊢ IsCoboundedUnder (fun x1 x2 ↦ x1 ≤ x2) atTop fun n ↦ κ.densityProcess ν n a x s

case refine_2
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
a : α
x : γ
s : Set β
⊢ ∀ᶠ (n : ℕ) in atTop, κ.densityProcess ν n a x s ≤ 1
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
A : Set γ
hA : MeasurableSet A
this : ∫ (x : γ) in A, κ.density ν a x s ∂ν a = limsup (fun i ↦ ∫ (x : γ) in A, κ.densityProcess ν i a x s ∂ν a) atTop
m : ℕ
hnm : n ≤ m
⊢ ∫ (x : γ) in A, κ.densityProcess ν m a x s ∂ν a = ∫ (x : γ) in A, κ.densityProcess ν n a x s ∂ν a

Tactic:
setIntegral_densityProcess_of_le hκν hnm _ hs hA,

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
A : Set γ
hA : MeasurableSet A
this : ∫ (x : γ) in A, κ.density ν a x s ∂ν a = limsup (fun i ↦ ∫ (x : γ) in A, κ.densityProcess ν i a x s ∂ν a) atTop
m : ℕ
hnm : n ≤ m
⊢ (κ a).real (A ×ˢ s) = ∫ (x : γ) in A, κ.densityProcess ν n a x s ∂ν a
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: case compl
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
s : Set β
hs : MeasurableSet s
A✝ : Set γ
this : IsFiniteKernel κ
hgen : mγ = generateFrom {s | ∃ n, MeasurableSet s}
hpi : IsPiSystem {s | ∃ n, MeasurableSet s}
A : Set γ
hA : MeasurableSet A
hA_eq : ∫ (x : γ) in A, κ.density ν a x s ∂ν a = (κ a).real (A ×ˢ s)
h : (κ a).real (A ×ˢ s) + ∫ (x : γ) in Aᶜ, κ.density ν a x s ∂ν a = ∫ (x : γ), κ.density ν a x s ∂ν a
⊢ ∫ (x : γ) in Aᶜ, κ.density ν a x s ∂ν a = (κ a).real (Aᶜ ×ˢ s)

Tactic:
integral_density hκν a hs

Goal state after:
case compl
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
s : Set β
hs : MeasurableSet s
A✝ : Set γ
this : IsFiniteKernel κ
hgen : mγ = generateFrom {s | ∃ n, MeasurableSet s}
hpi : IsPiSystem {s | ∃ n, MeasurableSet s}
A : Set γ
hA : MeasurableSet A
hA_eq : ∫ (x : γ) in A, κ.density ν a x s ∂ν a = (κ a).real (A ×ˢ s)
h : (κ a).real (A ×ˢ s) + ∫ (x : γ) in Aᶜ, κ.density ν a x s ∂ν a = (κ a).real (univ ×ˢ s)
⊢ ∫ (x : γ) in Aᶜ, κ.density ν a x s ∂ν a = (κ a).real (Aᶜ ×ˢ s)
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → Set β
hseq : Monotone seq
hseq_iUnion : ⋃ i, seq i = univ
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
this : IsFiniteKernel κ
⊢ Tendsto (fun m ↦ ∫ (x : γ), κ.density ν a x (seq m) ∂ν a) atTop (𝓝 ((κ a).real univ))

Tactic:
integral_density hκν a (hseq_meas _)

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → Set β
hseq : Monotone seq
hseq_iUnion : ⋃ i, seq i = univ
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
this : IsFiniteKernel κ
⊢ Tendsto (fun m ↦ (κ a).real (univ ×ˢ seq m)) atTop (𝓝 ((κ a).real univ))
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → Set β
hseq : Antitone seq
hseq_iInter : ⋂ i, seq i = ∅
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
this : IsFiniteKernel κ
⊢ Tendsto (fun m ↦ ∫ (x : γ), κ.density ν a x (seq m) ∂ν a) atTop (𝓝 0)

Tactic:
integral_density hκν a (hseq_meas _)

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → Set β
hseq : Antitone seq
hseq_iInter : ⋂ i, seq i = ∅
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
this : IsFiniteKernel κ
⊢ Tendsto (fun m ↦ (κ a).real (univ ×ˢ seq m)) atTop (𝓝 0)
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: case inl
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
a b : ℕ
hab : a ≤ b
⊢ IsSFiniteKernel (partialTraj κ a b)

pp: case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
a b : ℕ
hba : b ≤ a
⊢ IsSFiniteKernel (partialTraj κ a b)

Tactic:
· induction b, hab using Nat.le_induction with
    | base => rw [partialTraj_self]; infer_instance
    | succ k hak => rw [partialTraj_succ_of_le hak]; infer_instance

Goal state after:
case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
a b : ℕ
hba : b ≤ a
⊢ IsSFiniteKernel (partialTraj κ a b)
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: case inl
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsFiniteKernel (κ n)
a b : ℕ
hab : a ≤ b
⊢ IsFiniteKernel (partialTraj κ a b)

pp: case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsFiniteKernel (κ n)
a b : ℕ
hba : b ≤ a
⊢ IsFiniteKernel (partialTraj κ a b)

Tactic:
· induction b, hab using Nat.le_induction with
    | base => rw [partialTraj_self]; infer_instance
    | succ k hak => rw [partialTraj_succ_of_le hak]; infer_instance

Goal state after:
case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsFiniteKernel (κ n)
a b : ℕ
hba : b ≤ a
⊢ IsFiniteKernel (partialTraj κ a b)
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: case inl
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsZeroOrMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
⊢ IsZeroOrMarkovKernel (partialTraj κ a b)

pp: case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsZeroOrMarkovKernel (κ n)
a b : ℕ
hba : b ≤ a
⊢ IsZeroOrMarkovKernel (partialTraj κ a b)

Tactic:
· induction b, hab using Nat.le_induction with
    | base => rw [partialTraj_self]; infer_instance
    | succ k hak => rw [partialTraj_succ_of_le hak]; infer_instance

Goal state after:
case inr
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
a✝ b✝ c : ℕ
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsZeroOrMarkovKernel (κ n)
a b : ℕ
hba : b ≤ a
⊢ IsZeroOrMarkovKernel (partialTraj κ a b)
Module: Mathlib.Probability.Distributions.SetBernoulli

Goal state before:
pp: ι : Type u_1
inst✝ : Countable ι
u : Set ι
hu : u.Finite
p : ↑I
k : ℕ
this : {s | s ⊆ u ∧ s.ncard ∈ {k}}.Finite
h1 : ∀ s ∈ this.toFinset, setBer(u, p).real {s} = ↑p ^ k * (1 - ↑p) ^ (u.ncard - k)
⊢ ∑ p_1 ∈ this.toFinset, setBer(u, p).real {p_1} = ↑(u.ncard.choose k) * ↑p ^ k * (1 - ↑p) ^ (u.ncard - k)

Tactic:
Finset.sum_congr rfl h1,

Goal state after:
ι : Type u_1
inst✝ : Countable ι
u : Set ι
hu : u.Finite
p : ↑I
k : ℕ
this : {s | s ⊆ u ∧ s.ncard ∈ {k}}.Finite
h1 : ∀ s ∈ this.toFinset, setBer(u, p).real {s} = ↑p ^ k * (1 - ↑p) ^ (u.ncard - k)
⊢ ∑ x ∈ this.toFinset, ↑p ^ k * (1 - ↑p) ^ (u.ncard - k) = ↑(u.ncard.choose k) * ↑p ^ k * (1 - ↑p) ^ (u.ncard - k)
Module: Mathlib.Probability.Kernel.IonescuTulcea.Traj

Goal state before:
pp: X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
A : ℕ → Set ((n : ℕ) → X n)
A_mem : ∀ (n : ℕ), A n ∈ measurableCylinders X
A_anti : Antitone A
A_inter : ⋂ n, A n = ∅
p : ℕ
x₀ : (i : ↥(Iic p)) → X ↑i
x✝ : ∀ (n : ℕ), Nonempty (X n)
a : ℕ → ℕ
S : (n : ℕ) → Set ((i : ↥(Iic (a n))) → X ↑i)
mS : ∀ (n : ℕ), MeasurableSet (S n)
A_eq : ∀ (n : ℕ), A n = cylinder (Iic (a n)) (S n)
χ : ℕ → ((n : ℕ) → X n) → ℝ≥0∞ := fun n ↦ (A n).indicator 1
mχ : ∀ (n : ℕ), Measurable (χ n)
χ_dep : ∀ (n : ℕ), DependsOn (χ n) ↑(Iic (a n))
x y : (i : ℕ) → X i
n : ℕ
⊢ lmarginalPartialTraj κ p (a n) (χ n) (updateFinset x (Iic p) x₀) =
    lmarginalPartialTraj κ p (a n) (χ n) (updateFinset y (Iic p) x₀)

Tactic:
refine (χ_dep n).dependsOn_lmarginalPartialTraj p (mχ n) fun i hi ↦ ?_

Goal state after:
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
A : ℕ → Set ((n : ℕ) → X n)
A_mem : ∀ (n : ℕ), A n ∈ measurableCylinders X
A_anti : Antitone A
A_inter : ⋂ n, A n = ∅
p : ℕ
x₀ : (i : ↥(Iic p)) → X ↑i
x✝ : ∀ (n : ℕ), Nonempty (X n)
a : ℕ → ℕ
S : (n : ℕ) → Set ((i : ↥(Iic (a n))) → X ↑i)
mS : ∀ (n : ℕ), MeasurableSet (S n)
A_eq : ∀ (n : ℕ), A n = cylinder (Iic (a n)) (S n)
χ : ℕ → ((n : ℕ) → X n) → ℝ≥0∞ := fun n ↦ (A n).indicator 1
mχ : ∀ (n : ℕ), Measurable (χ n)
χ_dep : ∀ (n : ℕ), DependsOn (χ n) ↑(Iic (a n))
x y : (i : ℕ) → X i
n i : ℕ
hi : i ∈ ↑(Iic p)
⊢ updateFinset x (Iic p) x₀ i = updateFinset y (Iic p) x₀ i
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: case inl
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Kernel.id ∘ₖ partialTraj κ a b = partialTraj κ a b

pp: case inl.hf
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (_root_.IicProdIoc b (b + 1))

pp: case inl.hg
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (frestrictLe₂ ⋯)

Tactic:
id_comp

Goal state after:
case inl
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ partialTraj κ a b = partialTraj κ a b

case inl.hf
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (_root_.IicProdIoc b (b + 1))

case inl.hg
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (frestrictLe₂ ⋯)
Module: Mathlib.Probability.Kernel.RadonNikodym

Goal state before:
pp: α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ η : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
s : Set γ
hsm : MeasurableSet s
hs : s ⊆ κ.mutuallySingularSetSlice η a
hs' : ∀ x ∈ s, 1 ≤ κ.rnDerivAux (κ + η) a x
⊢ ∫⁻ (x : γ) in s,
      ↑(κ.rnDerivAux (κ + η) a x).toNNReal - ↑(1 - κ.rnDerivAux (κ + η) a x).toNNReal * κ.rnDeriv η a x ∂(κ + η) a =
    ∫⁻ (x : γ) in s, 1 ∂(κ + η) a

Tactic:
refine setLIntegral_congr_fun_ae hsm ?_

Goal state after:
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ η : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
s : Set γ
hsm : MeasurableSet s
hs : s ⊆ κ.mutuallySingularSetSlice η a
hs' : ∀ x ∈ s, 1 ≤ κ.rnDerivAux (κ + η) a x
⊢ ∀ᵐ (x : γ) ∂(κ + η) a,
    x ∈ s → ↑(κ.rnDerivAux (κ + η) a x).toNNReal - ↑(1 - κ.rnDerivAux (κ + η) a x).toNNReal * κ.rnDeriv η a x = 1
Module: Mathlib.Probability.Kernel.RadonNikodym

Goal state before:
pp: case refine_1
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
κ η : Kernel α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : (κ.singularPart η) a = 0
⊢ (η.withDensity (κ.rnDeriv η)) a + (κ.singularPart η) a ≪ η a

pp: case refine_2
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
κ η : Kernel α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : (η.withDensity (κ.rnDeriv η)) a + (κ.singularPart η) a ≪ η a
⊢ (κ.singularPart η) a = 0

Tactic:
· rw [h, add_zero]
    exact withDensity_absolutelyContinuous _ _

Goal state after:
case refine_2
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
κ η : Kernel α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : (η.withDensity (κ.rnDeriv η)) a + (κ.singularPart η) a ≪ η a
⊢ (κ.singularPart η) a = 0
Module: Mathlib.Probability.Kernel.Deterministic

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
γ : Type u_3
inst✝³ : MeasurableSpace γ
κ : Kernel α β
η : Kernel β γ
inst✝² : IsMarkovKernel κ
inst✝¹ : IsMarkovKernel η
inst✝ : IsDeterministic (η ∘ₖ κ)
a : α
s : Set γ
t : Set β
hs : MeasurableSet s
ht : MeasurableSet t
this : ∀ (b : β), (η b) s * t.indicator 1 b = t.indicator (fun b ↦ (η b) s) b
h₁ : ((η ∘ₖ κ) a) s = 1
integral_le_kernel : ∫⁻ (b : β) in t, (η b) s ∂κ a ≤ (κ a) t
⊢ ∫⁻ (b : β), 1 - (η b) s ∂κ a = ∫⁻ (x : β), (η x) sᶜ ∂κ a

Tactic:
by
        congr with x
        rw [measure_compl hs (by simp)]
        simp

Goal state after:
Module: Mathlib.Probability.Decision.Risk.Basic

Goal state before:
pp: Θ : Type u_1
𝓨 : Type u_4
mΘ : MeasurableSpace Θ
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
hl : Measurable (uncurry ℓ)
π : Measure Θ
inst✝ : SFinite π
⊢ bayesRisk ℓ (Kernel.discard Θ) π = ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π

Tactic:
rw [Kernel.discard_eq_const, bayesRisk_const hl]

Goal state after:
Module: Mathlib.Probability.Decision.Risk.Basic

Goal state before:
pp: Θ : Type u_1
𝓧 : Type u_2
𝓨 : Type u_4
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
π : Measure Θ
inst✝³ : Subsingleton 𝓧
inst✝² : Nonempty 𝓨
inst✝¹ : IsMarkovKernel P
inst✝ : SFinite π
hl : Measurable (uncurry ℓ)
⊢ bayesRisk ℓ P π = ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π

Tactic:
by
  simp [bayesRisk_of_subsingleton' hl]

Goal state after:
Module: Mathlib.Probability.Decision.Risk.Basic

Goal state before:
pp: Θ : Type u_1
𝓧 : Type u_2
𝓧' : Type u_3
𝓨 : Type u_4
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓧' : MeasurableSpace 𝓧'
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
inst✝¹ : IsSFiniteKernel P
π : Measure Θ
η : Kernel (Θ × 𝓧) 𝓧'
inst✝ : IsMarkovKernel η
⊢ bayesRisk ℓ (P ⊗ₖ η) π ≤ bayesRisk ℓ P π

Tactic:
have : P = (Kernel.deterministic Prod.fst (by fun_prop)) ∘ₖ (P ⊗ₖ η) := by
    rw [Kernel.deterministic_comp_eq_map, ← Kernel.fst_eq, Kernel.fst_compProd]
  nth_rw 2 [this]
  exact bayesRisk_le_bayesRisk_comp _ _ _ _

Goal state after:
Module: Mathlib.Probability.Distributions.Pareto

Goal state before:
pp: t r x : ℝ
hx : x ≤ t
⊢ EqOn (paretoPDF t r) (fun x ↦ 0) (Iio x)

Tactic:
· intro a (_ : a < _)
    simp only [paretoPDF_eq, ENNReal.ofReal_eq_zero]
    rw [if_neg (by linarith)]

Goal state after:
Module: Mathlib.Probability.ProbabilityMassFunction.Constructions

Goal state before:
pp: α : Type u_1
p : PMF α
s : Set α
h : ∃ a ∈ s, a ∈ p.support
a : α
⊢ (p.filter s h) a ≠ 0 ↔ a ∈ s ∧ a ∈ p.support

Tactic:
by
  rw [Ne, filter_apply_eq_zero_iff, not_or, Classical.not_not, Classical.not_not]

Goal state after:
Module: Mathlib.Probability.Distributions.TwoValued

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
m₀ : MeasurableSpace Ω
hm : m ≤ m₀
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hXmeas✝ : AEMeasurable X μ
hX : ∀ᵐ (ω : Ω) ∂μ, X ω = 0 ∨ X ω = 1
this :
  ∀ {Ω : Type u_1} {m : MeasurableSpace Ω} {X : Ω → ℝ} {m₀ : MeasurableSpace Ω},
    m ≤ m₀ →
      ∀ {μ : Measure Ω} [IsFiniteMeasure μ],
        AEMeasurable X μ →
          (∀ᵐ (ω : Ω) ∂μ, X ω = 0 ∨ X ω = 1) → Measurable X → Var[X; μ | m] =ᵐ[μ] μ[X | m] * μ[1 - X | m]
hXmeas : ¬Measurable X
Y : Ω → ℝ
hYmeas : Measurable Y
hXY : X =ᵐ[μ] Y
⊢ μ[Y | m] * μ[1 - Y | m] =ᵐ[μ] μ[X | m] * μ[1 - X | m]

Tactic:
refine .mul ?_ ?_ <;>
          exact condExp_congr_ae <| by filter_upwards [hXY] with ω hω; simp [hω]

Goal state after:
Module: Mathlib.Probability.HasLawExists

Goal state before:
pp: case h
ι : Type v
𝓧 : ι → Type u
m𝓧 : (i : ι) → MeasurableSpace (𝓧 i)
μ : (i : ι) → Measure (𝓧 i)
hμ : ∀ (i : ι), IsProbabilityMeasure (μ i)
i : ι
⊢ μ i = map (Function.eval i) (infinitePi μ)

Tactic:
exact ((measurePreserving_eval_infinitePi μ i).map_eq).symm

Goal state after:
Module: Mathlib.Probability.IdentDistribIndep

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
ι : Type u_3
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
μ : Measure Ω
ν : Measure Ω'
inst✝ : Countable ι
E : ι → Type u_6
mE : (i : ι) → MeasurableSpace (E i)
X : (i : ι) → Ω → E i
Y : (i : ι) → Ω' → E i
h : ∀ (i : ι), IdentDistrib (X i) (Y i) μ ν
hX_ind : iIndepFun X μ
hY_ind : iIndepFun Y ν
this✝ : IsProbabilityMeasure μ
this : IsProbabilityMeasure ν
i : ι
s✝ : Set (E i)
a✝ : MeasurableSet s✝
⊢ (Measure.map (X i) μ) s✝ = (Measure.map (Y i) ν) s✝

Tactic:
rw [(h i).map_eq]

Goal state after:
Module: Mathlib.Probability.Distributions.Uniform

Goal state before:
pp: α : Type u_1
s : Multiset α
hs : s ≠ 0
t : Set α
⊢ (ofMultiset s hs).toOuterMeasure t = ∑' (i : α), ↑(Multiset.count i (Multiset.filter (fun x ↦ x ∈ t) s)) * (↑s.card)⁻¹

Tactic:
toOuterMeasure_apply

Goal state after:
α : Type u_1
s : Multiset α
hs : s ≠ 0
t : Set α
⊢ ∑' (x : α), t.indicator (⇑(ofMultiset s hs)) x =
    ∑' (i : α), ↑(Multiset.count i (Multiset.filter (fun x ↦ x ∈ t) s)) * (↑s.card)⁻¹
Module: Mathlib.Probability.Independence.ZeroOne

Goal state before:
pp: case inr.h_le'
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
s : ι → MeasurableSpace Ω
m0 : MeasurableSpace Ω
κ : Kernel α Ω
μα : Measure α
β : Type u_4
p : Set ι → Prop
f : Filter ι
ns : β → Set ι
h_le : ∀ (n : ι), s n ≤ m0
hf : ∀ (t : Set ι), p t → tᶜ ∈ f
hns : Directed (fun x1 x2 ↦ x1 ⊆ x2) ns
hnsp : ∀ (a : β), p (ns a)
hμ : μα ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μα] ⇑η
hη : IsMarkovKernel η
h_indep : iIndep s η μα
⊢ limsup s f ≤ m0

Tactic:
exact limsup_le_iSup.trans (iSup_le h_le)

Goal state after:
Module: Mathlib.Probability.Independence.ZeroOne

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
s : ι → MeasurableSpace Ω
m0 : MeasurableSpace Ω
κ : Kernel α Ω
μα : Measure α
inst✝² : SemilatticeSup ι
inst✝¹ : NoMaxOrder ι
inst✝ : Nonempty ι
h_le : ∀ (n : ι), s n ≤ m0
h_indep : iIndep s κ μα
⊢ Indep (limsup s atTop) (limsup s atTop) κ μα

Tactic:
by
  let ns : ι → Set ι := Set.Iic
  have hnsp : ∀ i, BddAbove (ns i) := fun i => bddAbove_Iic
  refine indep_limsup_self h_le h_indep ?_ ?_ hnsp ?_
  · simp only [mem_atTop_sets, Set.mem_compl_iff, BddAbove, upperBounds, Set.Nonempty]
    rintro t ⟨a, ha⟩
    obtain ⟨b, hb⟩ : ∃ b, a < b := exists_gt a
    refine ⟨b, fun c hc hct => ?_⟩
    suffices ∀ i ∈ t, i < c from lt_irrefl c (this c hct)
    exact fun i hi => (ha hi).trans_lt (hb.trans_le hc)
  · exact Monotone.directed_le fun i j hij k hki => le_trans hki hij
  · exact fun n => ⟨n, le_rfl⟩

Goal state after: