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 #
ProbabilityTheory.Kernel.withDensity κ (f : α → β → ℝ≥0∞)
: kernela ↦ (κ a).withDensity (f a)
. It is defined ifκ
is s-finite. Iff
is finite everywhere, then this is also an s-finite kernel. The class of s-finite kernels is the smallest class of kernels that contains finite kernels and which is stable bywithDensity
. Integral:∫⁻ b, g b ∂(withDensity κ f a) = ∫⁻ b, f a b * g b ∂(κ a)
Main statements #
ProbabilityTheory.Kernel.lintegral_withDensity
:∫⁻ b, g b ∂(withDensity κ f a) = ∫⁻ b, f a b * g b ∂(κ a)
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)
.
Equations
- κ.withDensity f = if hf : Measurable (Function.uncurry f) then { toFun := fun (a : α) => (κ a).withDensity (f a), measurable' := ⋯ } else 0
Instances For
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.
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.