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Mathlib.Probability.Kernel.WithDensity

With Density #

For an s-finite kernel κ : kernel α β and a function f : α → β → ℝ≥0∞ which is finite everywhere, we define withDensity κ f as the kernel a ↦ (κ a).withDensity (f a). This is an s-finite kernel.

Main definitions #

Main statements #

noncomputable def ProbabilityTheory.kernel.withDensity {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsSFiniteKernel κ] (f : αβENNReal) :

Kernel with image (κ a).withDensity (f a) if Function.uncurry f is measurable, and with image 0 otherwise. If Function.uncurry f is measurable, it satisfies ∫⁻ b, g b ∂(withDensity κ f hf a) = ∫⁻ b, f a b * g b ∂(κ a).

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Instances For
    theorem ProbabilityTheory.kernel.withDensity_apply' {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {f : αβENNReal} (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsSFiniteKernel κ] (hf : Measurable (Function.uncurry f)) (a : α) (s : Set β) :
    ((ProbabilityTheory.kernel.withDensity κ f) a) s = ∫⁻ (b : β) in s, f a bκ a
    theorem ProbabilityTheory.kernel.lintegral_withDensity {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {f : αβENNReal} (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsSFiniteKernel κ] (hf : Measurable (Function.uncurry f)) (a : α) {g : βENNReal} (hg : Measurable g) :
    ∫⁻ (b : β), g b(ProbabilityTheory.kernel.withDensity κ f) a = ∫⁻ (b : β), f a b * g bκ a
    theorem ProbabilityTheory.kernel.integral_withDensity {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : (ProbabilityTheory.kernel α β)} {E : Type u_4} [NormedAddCommGroup E] [NormedSpace E] {f : βE} [ProbabilityTheory.IsSFiniteKernel κ] {a : α} {g : αβNNReal} (hg : Measurable (Function.uncurry g)) :
    ∫ (b : β), f b(ProbabilityTheory.kernel.withDensity κ fun (a : α) (b : β) => (g a b)) a = ∫ (b : β), g a b f bκ a
    theorem ProbabilityTheory.kernel.withDensity_tsum {α : Type u_1} {β : Type u_2} {ι : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [Countable ι] (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsSFiniteKernel κ] {f : ιαβENNReal} (hf : ∀ (i : ι), Measurable (Function.uncurry (f i))) :
    theorem ProbabilityTheory.kernel.isFiniteKernel_withDensity_of_bounded {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {f : αβENNReal} (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsFiniteKernel κ] {B : ENNReal} (hB_top : B ) (hf_B : ∀ (a : α) (b : β), f a b B) :

    If a kernel κ is finite and a function f : α → β → ℝ≥0∞ is bounded, then withDensity κ f is finite.

    Auxiliary lemma for IsSFiniteKernel.withDensity. If a kernel κ is finite, then withDensity κ f is s-finite.

    theorem ProbabilityTheory.kernel.IsSFiniteKernel.withDensity {α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {f : αβENNReal} (κ : (ProbabilityTheory.kernel α β)) [ProbabilityTheory.IsSFiniteKernel κ] (hf_ne_top : ∀ (a : α) (b : β), f a b ) :

    For an s-finite kernel κ and a function f : α → β → ℝ≥0∞ which is everywhere finite, withDensity κ f is s-finite.

    For an s-finite kernel κ and a function f : α → β → ℝ≥0, withDensity κ f is s-finite.

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