File: Mathlib/Probability/Kernel/Disintegration/CdfToKernel.lean

full_name: ProbabilityTheory.IsRatCondKernelCDF.measurable
code: /-- a function `f : α × β → ℚ → ℝ` is called a rational conditional kernel CDF of `κ` with respect
to `ν` if is measurable, if `fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ`
and for all measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`.
Also the `ℚ → ℝ` function `f (a, b)` should satisfy the properties of a Sieltjes function for
`(ν a)`-almost all `b : β`. -/
structure IsRatCondKernelCDF (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) : Prop :=
  measurable : Measurable f
  isRatStieltjesPoint_ae (a : α) : ∀ᵐ b ∂(ν a), IsRatStieltjesPoint f (a, b)
  integrable (a : α) (q : ℚ) : Integrable (fun b ↦ f (a, b) q) (ν a)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (q : ℚ) :
    ∫ b in s, f (a, b) q ∂(ν a) = (κ a (s ×ˢ Iic (q : ℝ))).toReal
goal: Measurable f

full_name: ProbabilityTheory.IsRatCondKernelCDF
code: /-- a function `f : α × β → ℚ → ℝ` is called a rational conditional kernel CDF of `κ` with respect
to `ν` if is measurable, if `fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ`
and for all measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`.
Also the `ℚ → ℝ` function `f (a, b)` should satisfy the properties of a Sieltjes function for
`(ν a)`-almost all `b : β`. -/
structure IsRatCondKernelCDF (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) : Prop :=
  measurable : Measurable f
  isRatStieltjesPoint_ae (a : α) : ∀ᵐ b ∂(ν a), IsRatStieltjesPoint f (a, b)
  integrable (a : α) (q : ℚ) : Integrable (fun b ↦ f (a, b) q) (ν a)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (q : ℚ) :
    ∫ b in s, f (a, b) q ∂(ν a) = (κ a (s ×ˢ Iic (q : ℝ))).toReal
goal: Prop

full_name: ProbabilityTheory.IsRatCondKernelCDF.setIntegral
code: /-- a function `f : α × β → ℚ → ℝ` is called a rational conditional kernel CDF of `κ` with respect
to `ν` if is measurable, if `fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ`
and for all measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`.
Also the `ℚ → ℝ` function `f (a, b)` should satisfy the properties of a Sieltjes function for
`(ν a)`-almost all `b : β`. -/
structure IsRatCondKernelCDF (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) : Prop :=
  measurable : Measurable f
  isRatStieltjesPoint_ae (a : α) : ∀ᵐ b ∂(ν a), IsRatStieltjesPoint f (a, b)
  integrable (a : α) (q : ℚ) : Integrable (fun b ↦ f (a, b) q) (ν a)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (q : ℚ) :
    ∫ b in s, f (a, b) q ∂(ν a) = (κ a (s ×ˢ Iic (q : ℝ))).toReal
goal: ∫ (b : β) in s, f (a, b) q ∂ν a = ((κ a) (s ×ˢ Set.Iic ↑q)).toReal

full_name: ProbabilityTheory.IsRatCondKernelCDF.integrable
code: /-- a function `f : α × β → ℚ → ℝ` is called a rational conditional kernel CDF of `κ` with respect
to `ν` if is measurable, if `fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ`
and for all measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`.
Also the `ℚ → ℝ` function `f (a, b)` should satisfy the properties of a Sieltjes function for
`(ν a)`-almost all `b : β`. -/
structure IsRatCondKernelCDF (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) : Prop :=
  measurable : Measurable f
  isRatStieltjesPoint_ae (a : α) : ∀ᵐ b ∂(ν a), IsRatStieltjesPoint f (a, b)
  integrable (a : α) (q : ℚ) : Integrable (fun b ↦ f (a, b) q) (ν a)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (q : ℚ) :
    ∫ b in s, f (a, b) q ∂(ν a) = (κ a (s ×ˢ Iic (q : ℝ))).toReal
goal: MeasureTheory.Integrable (fun b => f (a, b) q) (ν a)

full_name: ProbabilityTheory.IsRatCondKernelCDF.isRatStieltjesPoint_ae
code: /-- a function `f : α × β → ℚ → ℝ` is called a rational conditional kernel CDF of `κ` with respect
to `ν` if is measurable, if `fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ`
and for all measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`.
Also the `ℚ → ℝ` function `f (a, b)` should satisfy the properties of a Sieltjes function for
`(ν a)`-almost all `b : β`. -/
structure IsRatCondKernelCDF (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) : Prop :=
  measurable : Measurable f
  isRatStieltjesPoint_ae (a : α) : ∀ᵐ b ∂(ν a), IsRatStieltjesPoint f (a, b)
  integrable (a : α) (q : ℚ) : Integrable (fun b ↦ f (a, b) q) (ν a)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (q : ℚ) :
    ∫ b in s, f (a, b) q ∂(ν a) = (κ a (s ×ˢ Iic (q : ℝ))).toReal
goal: ∀ᵐ (b : β) ∂ν a, ProbabilityTheory.IsRatStieltjesPoint f (a, b)

full_name: ProbabilityTheory.IsRatCondKernelCDF.mono
code: lemma IsRatCondKernelCDF.mono (hf : IsRatCondKernelCDF f κ ν) (a : α) :
    ∀ᵐ b ∂(ν a), Monotone (f (a, b)) 
goal: ∀ᵐ (b : β) ∂ν a, Monotone (f (a, b))

full_name: ProbabilityTheory.IsRatCondKernelCDF.tendsto_atTop_one
code: lemma IsRatCondKernelCDF.tendsto_atTop_one (hf : IsRatCondKernelCDF f κ ν) (a : α) :
    ∀ᵐ b ∂(ν a), Tendsto (f (a, b)) atTop (𝓝 1) 
goal: ∀ᵐ (b : β) ∂ν a, Filter.Tendsto (f (a, b)) Filter.atTop (𝓝 1)

full_name: ProbabilityTheory.IsRatCondKernelCDF.tendsto_atBot_zero
code: lemma IsRatCondKernelCDF.tendsto_atBot_zero (hf : IsRatCondKernelCDF f κ ν) (a : α) :
    ∀ᵐ b ∂(ν a), Tendsto (f (a, b)) atBot (𝓝 0) 
goal: ∀ᵐ (b : β) ∂ν a, Filter.Tendsto (f (a, b)) Filter.atBot (𝓝 0)

full_name: ProbabilityTheory.IsRatCondKernelCDF.iInf_rat_gt_eq
code: lemma IsRatCondKernelCDF.iInf_rat_gt_eq (hf : IsRatCondKernelCDF f κ ν) (a : α) :
    ∀ᵐ b ∂(ν a), ∀ q, ⨅ r : Ioi q, f (a, b) r = f (a, b) q 
goal: ∀ᵐ (b : β) ∂ν a, ∀ (q : ℚ), ⨅ r, f (a, b) ↑r = f (a, b) q

full_name: ProbabilityTheory.stieltjesOfMeasurableRat_ae_eq
code: lemma stieltjesOfMeasurableRat_ae_eq (hf : IsRatCondKernelCDF f κ ν) (a : α) (q : ℚ) :
    (fun b ↦ stieltjesOfMeasurableRat f hf.measurable (a, b) q) =ᵐ[ν a] fun b ↦ f (a, b) q 
goal: (fun b => ↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) ↑q) =ᵐ[ν a] fun b => f (a, b) q

full_name: ProbabilityTheory.setIntegral_stieltjesOfMeasurableRat_rat
code: lemma setIntegral_stieltjesOfMeasurableRat_rat (hf : IsRatCondKernelCDF f κ ν) (a : α) (q : ℚ)
    {s : Set β} (hs : MeasurableSet s) :
    ∫ b in s, stieltjesOfMeasurableRat f hf.measurable (a, b) q ∂(ν a)
      = (κ a (s ×ˢ Iic (q : ℝ))).toReal 
goal: ∫ (b : β) in s, ↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) ↑q ∂ν a = ((κ a) (s ×ˢ Set.Iic ↑q)).toReal

full_name: ProbabilityTheory.setLIntegral_stieltjesOfMeasurableRat_rat
code: lemma setLIntegral_stieltjesOfMeasurableRat_rat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (q : ℚ) {s : Set β} (hs : MeasurableSet s) :
    ∫⁻ b in s, ENNReal.ofReal (stieltjesOfMeasurableRat f hf.measurable (a, b) q) ∂(ν a)
      = κ a (s ×ˢ Iic (q : ℝ)) 
goal: ∫⁻ (b : β) in s, ENNReal.ofReal (↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) ↑q) ∂ν a =
  (κ a) (s ×ˢ Set.Iic ↑q)

full_name: ProbabilityTheory.setLIntegral_stieltjesOfMeasurableRat
code: lemma setLIntegral_stieltjesOfMeasurableRat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (x : ℝ) {s : Set β} (hs : MeasurableSet s) :
    ∫⁻ b in s, ENNReal.ofReal (stieltjesOfMeasurableRat f hf.measurable (a, b) x) ∂(ν a)
      = κ a (s ×ˢ Iic x) 
goal: ∫⁻ (b : β) in s, ENNReal.ofReal (↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) x) ∂ν a =
  (κ a) (s ×ˢ Set.Iic x)

full_name: ProbabilityTheory.lintegral_stieltjesOfMeasurableRat
code: lemma lintegral_stieltjesOfMeasurableRat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (x : ℝ) :
    ∫⁻ b, ENNReal.ofReal (stieltjesOfMeasurableRat f hf.measurable (a, b) x) ∂(ν a)
      = κ a (univ ×ˢ Iic x) 
goal: ∫⁻ (b : β), ENNReal.ofReal (↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) x) ∂ν a =
  (κ a) (Set.univ ×ˢ Set.Iic x)

full_name: ProbabilityTheory.integrable_stieltjesOfMeasurableRat
code: lemma integrable_stieltjesOfMeasurableRat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (x : ℝ) :
    Integrable (fun b ↦ stieltjesOfMeasurableRat f hf.measurable (a, b) x) (ν a) 
goal: MeasureTheory.Integrable (fun b => ↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) x) (ν a)

full_name: ProbabilityTheory.setIntegral_stieltjesOfMeasurableRat
code: lemma setIntegral_stieltjesOfMeasurableRat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (x : ℝ) {s : Set β} (hs : MeasurableSet s) :
    ∫ b in s, stieltjesOfMeasurableRat f hf.measurable (a, b) x ∂(ν a)
      = (κ a (s ×ˢ Iic x)).toReal 
goal: ∫ (b : β) in s, ↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) x ∂ν a = ((κ a) (s ×ˢ Set.Iic x)).toReal

full_name: ProbabilityTheory.integral_stieltjesOfMeasurableRat
code: lemma integral_stieltjesOfMeasurableRat [IsFiniteKernel κ] (hf : IsRatCondKernelCDF f κ ν)
    (a : α) (x : ℝ) :
    ∫ b, stieltjesOfMeasurableRat f hf.measurable (a, b) x ∂(ν a)
      = (κ a (univ ×ˢ Iic x)).toReal 
goal: ∫ (b : β), ↑(ProbabilityTheory.stieltjesOfMeasurableRat f ⋯ (a, b)) x ∂ν a = ((κ a) (Set.univ ×ˢ Set.Iic x)).toReal

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.measurable
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: Measurable f

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.nonneg'
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: ∀ᵐ (c : β) ∂ν a, 0 ≤ f (a, c) q

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.le_one'
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: ∀ᵐ (c : β) ∂ν a, f (a, c) q ≤ 1

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_integral_of_monotone
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: Filter.Tendsto (fun m => ∫ (c : β), f (a, c) (seq m) ∂ν a) Filter.atTop (𝓝 ((ν a) Set.univ).toReal)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: Prop

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.mono'
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: ∀ᵐ (c : β) ∂ν a, f (a, c) q ≤ f (a, c) r

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.setIntegral
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: ∫ (c : β) in A, f (a, c) q ∂ν a = ((κ a) (A ×ˢ Set.Iic ↑q)).toReal

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_integral_of_antitone
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: Filter.Tendsto (fun m => ∫ (c : β), f (a, c) (seq m) ∂ν a) Filter.atTop (𝓝 0)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.integrable
code: /-- This property implies `IsRatCondKernelCDF`. The measurability, integrability and integral
conditions are the same, but the limit properties of `IsRatCondKernelCDF` are replaced by
limits of integrals. -/
structure IsRatCondKernelCDFAux (f : α × β → ℚ → ℝ) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable : Measurable f
  mono' (a : α) {q r : ℚ} (_hqr : q ≤ r) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ f (a, c) r
  nonneg' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), 0 ≤ f (a, c) q
  le_one' (a : α) (q : ℚ) : ∀ᵐ c ∂(ν a), f (a, c) q ≤ 1
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atBot (𝓝 0)` but slightly easier
  to prove in the current applications of this definition (some integral convergence lemmas
  currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_antitone (a : α) (seq : ℕ → ℚ) (_hs : Antitone seq)
    (_hs_tendsto : Tendsto seq atTop atBot) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 0)
  /- Same as `Tendsto (fun q : ℚ ↦ ∫ c, f (a, c) q ∂(ν a)) atTop (𝓝 (ν a univ).toReal)` but
  slightly easier to prove in the current applications of this definition (some integral convergence
  lemmas currently apply only to `ℕ`, not `ℚ`) -/
  tendsto_integral_of_monotone (a : α) (seq : ℕ → ℚ) (_hs : Monotone seq)
    (_hs_tendsto : Tendsto seq atTop atTop) :
    Tendsto (fun m ↦ ∫ c, f (a, c) (seq m) ∂(ν a)) atTop (𝓝 (ν a univ).toReal)
  integrable (a : α) (q : ℚ) : Integrable (fun c ↦ f (a, c) q) (ν a)
  setIntegral (a : α) {A : Set β} (_hA : MeasurableSet A) (q : ℚ) :
    ∫ c in A, f (a, c) q ∂(ν a) = (κ a (A ×ˢ Iic ↑q)).toReal
goal: MeasureTheory.Integrable (fun c => f (a, c) q) (ν a)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.measurable_right
code: lemma IsRatCondKernelCDFAux.measurable_right (hf : IsRatCondKernelCDFAux f κ ν) (a : α) (q : ℚ) :
    Measurable (fun t ↦ f (a, t) q) 
goal: Measurable fun t => f (a, t) q

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.mono
code: lemma IsRatCondKernelCDFAux.mono (hf : IsRatCondKernelCDFAux f κ ν) (a : α) :
    ∀ᵐ c ∂(ν a), Monotone (f (a, c)) 
goal: ∀ᵐ (c : β) ∂ν a, Monotone (f (a, c))

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.nonneg
code: lemma IsRatCondKernelCDFAux.nonneg (hf : IsRatCondKernelCDFAux f κ ν) (a : α) :
    ∀ᵐ c ∂(ν a), ∀ q, 0 ≤ f (a, c) q 
goal: ∀ᵐ (c : β) ∂ν a, ∀ (q : ℚ), 0 ≤ f (a, c) q

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.le_one
code: lemma IsRatCondKernelCDFAux.le_one (hf : IsRatCondKernelCDFAux f κ ν) (a : α) :
    ∀ᵐ c ∂(ν a), ∀ q, f (a, c) q ≤ 1 
goal: ∀ᵐ (c : β) ∂ν a, ∀ (q : ℚ), f (a, c) q ≤ 1

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_zero_of_antitone
code: lemma IsRatCondKernelCDFAux.tendsto_zero_of_antitone (hf : IsRatCondKernelCDFAux f κ ν)
    [IsFiniteKernel ν] (a : α) (seq : ℕ → ℚ) (hseq : Antitone seq)
    (hseq_tendsto : Tendsto seq atTop atBot) :
    ∀ᵐ c ∂(ν a), Tendsto (fun m ↦ f (a, c) (seq m)) atTop (𝓝 0) 
goal: ∀ᵐ (c : β) ∂ν a, Filter.Tendsto (fun m => f (a, c) (seq m)) Filter.atTop (𝓝 0)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_one_of_monotone
code: lemma IsRatCondKernelCDFAux.tendsto_one_of_monotone (hf : IsRatCondKernelCDFAux f κ ν)
    [IsFiniteKernel ν] (a : α) (seq : ℕ → ℚ) (hseq : Monotone seq)
    (hseq_tendsto : Tendsto seq atTop atTop) :
    ∀ᵐ c ∂(ν a), Tendsto (fun m ↦ f (a, c) (seq m)) atTop (𝓝 1) 
goal: ∀ᵐ (c : β) ∂ν a, Filter.Tendsto (fun m => f (a, c) (seq m)) Filter.atTop (𝓝 1)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_atTop_one
code: lemma IsRatCondKernelCDFAux.tendsto_atTop_one (hf : IsRatCondKernelCDFAux f κ ν) [IsFiniteKernel ν]
    (a : α) :
    ∀ᵐ t ∂(ν a), Tendsto (f (a, t)) atTop (𝓝 1) 
goal: ∀ᵐ (t : β) ∂ν a, Filter.Tendsto (f (a, t)) Filter.atTop (𝓝 1)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.tendsto_atBot_zero
code: lemma IsRatCondKernelCDFAux.tendsto_atBot_zero (hf : IsRatCondKernelCDFAux f κ ν) [IsFiniteKernel ν]
    (a : α) :
    ∀ᵐ t ∂(ν a), Tendsto (f (a, t)) atBot (𝓝 0) 
goal: ∀ᵐ (t : β) ∂ν a, Filter.Tendsto (f (a, t)) Filter.atBot (𝓝 0)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.bddBelow_range
code: lemma IsRatCondKernelCDFAux.bddBelow_range (hf : IsRatCondKernelCDFAux f κ ν) (a : α) :
    ∀ᵐ t ∂(ν a), ∀ q : ℚ, BddBelow (range fun (r : Ioi q) ↦ f (a, t) r) 
goal: ∀ᵐ (t : β) ∂ν a, ∀ (q : ℚ), BddBelow (Set.range fun r => f (a, t) ↑r)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.integrable_iInf_rat_gt
code: lemma IsRatCondKernelCDFAux.integrable_iInf_rat_gt (hf : IsRatCondKernelCDFAux f κ ν)
    [IsFiniteKernel ν] (a : α) (q : ℚ) :
    Integrable (fun t ↦ ⨅ r : Ioi q, f (a, t) r) (ν a) 
goal: MeasureTheory.Integrable (fun t => ⨅ r, f (a, t) ↑r) (ν a)

full_name: MeasureTheory.Measure.iInf_rat_gt_prod_Iic
code: lemma _root_.MeasureTheory.Measure.iInf_rat_gt_prod_Iic {ρ : Measure (α × ℝ)} [IsFiniteMeasure ρ]
    {s : Set α} (hs : MeasurableSet s) (t : ℚ) :
    ⨅ r : { r' : ℚ // t < r' }, ρ (s ×ˢ Iic (r : ℝ)) = ρ (s ×ˢ Iic (t : ℝ)) 
goal: ⨅ r, ρ (s ×ˢ Set.Iic ↑↑r) = ρ (s ×ˢ Set.Iic ↑t)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.setIntegral_iInf_rat_gt
code: lemma IsRatCondKernelCDFAux.setIntegral_iInf_rat_gt (hf : IsRatCondKernelCDFAux f κ ν)
    [IsFiniteKernel κ] [IsFiniteKernel ν] (a : α) (q : ℚ) {A : Set β} (hA : MeasurableSet A) :
    ∫ t in A, ⨅ r : Ioi q, f (a, t) r ∂(ν a) = (κ a (A ×ˢ Iic (q : ℝ))).toReal 
goal: ∫ (t : β) in A, ⨅ r, f (a, t) ↑r ∂ν a = ((κ a) (A ×ˢ Set.Iic ↑q)).toReal

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.iInf_rat_gt_eq
code: lemma IsRatCondKernelCDFAux.iInf_rat_gt_eq (hf : IsRatCondKernelCDFAux f κ ν) [IsFiniteKernel κ]
    [IsFiniteKernel ν] (a : α) :
    ∀ᵐ t ∂(ν a), ∀ q : ℚ, ⨅ r : Ioi q, f (a, t) r = f (a, t) q 
goal: ∀ᵐ (t : β) ∂ν a, ∀ (q : ℚ), ⨅ r, f (a, t) ↑r = f (a, t) q

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.isRatStieltjesPoint_ae
code: lemma IsRatCondKernelCDFAux.isRatStieltjesPoint_ae (hf : IsRatCondKernelCDFAux f κ ν)
    [IsFiniteKernel κ] [IsFiniteKernel ν] (a : α) :
    ∀ᵐ t ∂(ν a), IsRatStieltjesPoint f (a, t) 
goal: ∀ᵐ (t : β) ∂ν a, ProbabilityTheory.IsRatStieltjesPoint f (a, t)

full_name: ProbabilityTheory.IsRatCondKernelCDFAux.isRatCondKernelCDF
code: lemma IsRatCondKernelCDFAux.isRatCondKernelCDF (hf : IsRatCondKernelCDFAux f κ ν) [IsFiniteKernel κ]
    [IsFiniteKernel ν] :
    IsRatCondKernelCDF f κ ν 
goal: ProbabilityTheory.IsRatCondKernelCDF f κ ν

full_name: ProbabilityTheory.IsCondKernelCDF.integrable
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: MeasureTheory.Integrable (fun b => ↑(f (a, b)) x) (ν a)

full_name: ProbabilityTheory.IsCondKernelCDF.tendsto_atBot_zero
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: Filter.Tendsto (↑(f p)) Filter.atBot (𝓝 0)

full_name: ProbabilityTheory.IsCondKernelCDF.measurable
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: Measurable fun p => ↑(f p) x

full_name: ProbabilityTheory.IsCondKernelCDF.setIntegral
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: ∫ (b : β) in s, ↑(f (a, b)) x ∂ν a = ((κ a) (s ×ˢ Set.Iic x)).toReal

full_name: ProbabilityTheory.IsCondKernelCDF
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: Prop

full_name: ProbabilityTheory.IsCondKernelCDF.tendsto_atTop_one
code: /-- A function `f : α × β → StieltjesFunction` is called a conditional kernel CDF of `κ` with
respect to `ν` if it is measurable, tends to to 0 at -∞ and to 1 at +∞ for all `p : α × β`,
`fun b ↦ f (a, b) x` is `(ν a)`-integrable for all `a : α` and `x : ℝ` and for all
measurable sets `s : Set β`, `∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal`. -/
structure IsCondKernelCDF (f : α × β → StieltjesFunction) (κ : Kernel α (β × ℝ)) (ν : Kernel α β) :
    Prop :=
  measurable (x : ℝ) : Measurable fun p ↦ f p x
  integrable (a : α) (x : ℝ) : Integrable (fun b ↦ f (a, b) x) (ν a)
  tendsto_atTop_one (p : α × β) : Tendsto (f p) atTop (𝓝 1)
  tendsto_atBot_zero (p : α × β) : Tendsto (f p) atBot (𝓝 0)
  setIntegral (a : α) {s : Set β} (_hs : MeasurableSet s) (x : ℝ) :
    ∫ b in s, f (a, b) x ∂(ν a) = (κ a (s ×ˢ Iic x)).toReal
goal: Filter.Tendsto (↑(f p)) Filter.atTop (𝓝 1)

full_name: ProbabilityTheory.IsCondKernelCDF.nonneg
code: lemma IsCondKernelCDF.nonneg (hf : IsCondKernelCDF f κ ν) (p : α × β) (x : ℝ) : 0 ≤ f p x 
goal: 0 ≤ ↑(f p) x

full_name: ProbabilityTheory.IsCondKernelCDF.le_one
code: lemma IsCondKernelCDF.le_one (hf : IsCondKernelCDF f κ ν) (p : α × β) (x : ℝ) : f p x ≤ 1 
goal: ↑(f p) x ≤ 1

full_name: ProbabilityTheory.IsCondKernelCDF.integral
code: lemma IsCondKernelCDF.integral
    {f : α × β → StieltjesFunction} (hf : IsCondKernelCDF f κ ν) (a : α) (x : ℝ) :
    ∫ b, f (a, b) x ∂(ν a) = (κ a (univ ×ˢ Iic x)).toReal 
goal: ∫ (b : β), ↑(f (a, b)) x ∂ν a = ((κ a) (Set.univ ×ˢ Set.Iic x)).toReal

full_name: ProbabilityTheory.IsCondKernelCDF.setLIntegral
code: lemma IsCondKernelCDF.setLIntegral [IsFiniteKernel κ]
    {f : α × β → StieltjesFunction} (hf : IsCondKernelCDF f κ ν)
    (a : α) {s : Set β} (hs : MeasurableSet s) (x : ℝ) :
    ∫⁻ b in s, ENNReal.ofReal (f (a, b) x) ∂(ν a) = κ a (s ×ˢ Iic x) 
goal: ∫⁻ (b : β) in s, ENNReal.ofReal (↑(f (a, b)) x) ∂ν a = (κ a) (s ×ˢ Set.Iic x)

full_name: ProbabilityTheory.IsCondKernelCDF.lintegral
code: lemma IsCondKernelCDF.lintegral [IsFiniteKernel κ]
    {f : α × β → StieltjesFunction} (hf : IsCondKernelCDF f κ ν) (a : α) (x : ℝ) :
    ∫⁻ b, ENNReal.ofReal (f (a, b) x) ∂(ν a) = κ a (univ ×ˢ Iic x) 
goal: ∫⁻ (b : β), ENNReal.ofReal (↑(f (a, b)) x) ∂ν a = (κ a) (Set.univ ×ˢ Set.Iic x)

full_name: ProbabilityTheory.isCondKernelCDF_stieltjesOfMeasurableRat
code: lemma isCondKernelCDF_stieltjesOfMeasurableRat {f : α × β → ℚ → ℝ} (hf : IsRatCondKernelCDF f κ ν)
    [IsFiniteKernel κ] :
    IsCondKernelCDF (stieltjesOfMeasurableRat f hf.measurable) κ ν 
goal: ProbabilityTheory.IsCondKernelCDF (ProbabilityTheory.stieltjesOfMeasurableRat f ⋯) κ ν

full_name: ProbabilityTheory.StieltjesFunction.measurable_measure
code: /-- A measurable function `α → StieltjesFunction` with limits 0 at -∞ and 1 at +∞ gives a measurable
function `α → Measure ℝ` by taking `StieltjesFunction.measure` at each point. -/
lemma StieltjesFunction.measurable_measure {f : α → StieltjesFunction}
    (hf : ∀ q, Measurable fun a ↦ f a q)
    (hf_bot : ∀ a, Tendsto (f a) atBot (𝓝 0))
    (hf_top : ∀ a, Tendsto (f a) atTop (𝓝 1)) :
    Measurable fun a ↦ (f a).measure 
goal: Measurable fun a => (f a).measure

full_name: ProbabilityTheory.IsCondKernelCDF.toKernel
code: /-- A function `f : α × β → StieltjesFunction` with the property `IsCondKernelCDF f κ ν` gives a
Markov kernel from `α × β` to `ℝ`, by taking for each `p : α × β` the measure defined by `f p`. -/
noncomputable
def IsCondKernelCDF.toKernel (f : α × β → StieltjesFunction) (hf : IsCondKernelCDF f κ ν) :
    Kernel (α × β) ℝ 
goal: {x : MeasurableSpace β} →
  {κ : ProbabilityTheory.Kernel α (β × ℝ)} →
    {ν : ProbabilityTheory.Kernel α β} →
      (f : α × β → StieltjesFunction) → ProbabilityTheory.IsCondKernelCDF f κ ν → ProbabilityTheory.Kernel (α × β) ℝ

full_name: ProbabilityTheory.IsCondKernelCDF.toKernel_apply
code: lemma IsCondKernelCDF.toKernel_apply {hf : IsCondKernelCDF f κ ν} (p : α × β) :
    hf.toKernel f p = (f p).measure 
goal: ∀ {x : MeasurableSpace β} {f : α × β → StieltjesFunction} {κ : ProbabilityTheory.Kernel α (β × ℝ)}
  {ν : ProbabilityTheory.Kernel α β} {hf : ProbabilityTheory.IsCondKernelCDF f κ ν} (p : α × β),
  (ProbabilityTheory.IsCondKernelCDF.toKernel f hf) p = (f p).measure

full_name: ProbabilityTheory.instIsMarkovKernel_toKernel
code: instance instIsMarkovKernel_toKernel {hf : IsCondKernelCDF f κ ν} :
    IsMarkovKernel (hf.toKernel f) 
goal: ∀ {x : MeasurableSpace β} {f : α × β → StieltjesFunction} {κ : ProbabilityTheory.Kernel α (β × ℝ)}
  {ν : ProbabilityTheory.Kernel α β} {hf : ProbabilityTheory.IsCondKernelCDF f κ ν},
  ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.IsCondKernelCDF.toKernel f hf)

full_name: ProbabilityTheory.IsCondKernelCDF.toKernel_Iic
code: lemma IsCondKernelCDF.toKernel_Iic {hf : IsCondKernelCDF f κ ν} (p : α × β) (x : ℝ) :
    hf.toKernel f p (Iic x) = ENNReal.ofReal (f p x) 
goal: ∀ {x : MeasurableSpace β} {f : α × β → StieltjesFunction} {κ : ProbabilityTheory.Kernel α (β × ℝ)}
  {ν : ProbabilityTheory.Kernel α β} {hf : ProbabilityTheory.IsCondKernelCDF f κ ν} (p : α × β) (x_1 : ℝ),
  ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) p) (Set.Iic x_1) = ENNReal.ofReal (↑(f p) x_1)

full_name: ProbabilityTheory.setLIntegral_toKernel_Iic
code: lemma setLIntegral_toKernel_Iic [IsFiniteKernel κ] (hf : IsCondKernelCDF f κ ν)
    (a : α) (x : ℝ) {s : Set β} (hs : MeasurableSet s) :
    ∫⁻ b in s, hf.toKernel f (a, b) (Iic x) ∂(ν a) = κ a (s ×ˢ Iic x) 
goal: ∫⁻ (b : β) in s, ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) (a, b)) (Set.Iic x) ∂ν a = (κ a) (s ×ˢ Set.Iic x)

full_name: ProbabilityTheory.setLIntegral_toKernel_univ
code: lemma setLIntegral_toKernel_univ [IsFiniteKernel κ] (hf : IsCondKernelCDF f κ ν)
    (a : α) {s : Set β} (hs : MeasurableSet s) :
    ∫⁻ b in s, hf.toKernel f (a, b) univ ∂(ν a) = κ a (s ×ˢ univ) 
goal: ∫⁻ (b : β) in s, ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) (a, b)) Set.univ ∂ν a = (κ a) (s ×ˢ Set.univ)

full_name: ProbabilityTheory.lintegral_toKernel_univ
code: lemma lintegral_toKernel_univ [IsFiniteKernel κ] (hf : IsCondKernelCDF f κ ν) (a : α) :
    ∫⁻ b, hf.toKernel f (a, b) univ ∂(ν a) = κ a univ 
goal: ∫⁻ (b : β), ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) (a, b)) Set.univ ∂ν a = (κ a) Set.univ

full_name: ProbabilityTheory.setLIntegral_toKernel_prod
code: lemma setLIntegral_toKernel_prod [IsFiniteKernel κ] (hf : IsCondKernelCDF f κ ν)
    (a : α) {s : Set β} (hs : MeasurableSet s) {t : Set ℝ} (ht : MeasurableSet t) :
    ∫⁻ b in s, hf.toKernel f (a, b) t ∂(ν a) = κ a (s ×ˢ t) 
goal: ∫⁻ (b : β) in s, ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) (a, b)) t ∂ν a = (κ a) (s ×ˢ t)

full_name: ProbabilityTheory.lintegral_toKernel_mem
code: lemma lintegral_toKernel_mem [IsFiniteKernel κ] (hf : IsCondKernelCDF f κ ν)
    (a : α) {s : Set (β × ℝ)} (hs : MeasurableSet s) :
    ∫⁻ b, hf.toKernel f (a, b) {y | (b, y) ∈ s} ∂(ν a) = κ a s 
goal: ∫⁻ (b : β), ((ProbabilityTheory.IsCondKernelCDF.toKernel f hf) (a, b)) {y | (b, y) ∈ s} ∂ν a = (κ a) s

full_name: ProbabilityTheory.compProd_toKernel
code: lemma compProd_toKernel [IsFiniteKernel κ] [IsSFiniteKernel ν] (hf : IsCondKernelCDF f κ ν) :
    ν ⊗ₖ hf.toKernel f = κ 
goal: ν ⊗ₖ ProbabilityTheory.IsCondKernelCDF.toKernel f hf = κ