Copulas obtained by tagging two conditional models on predictor blocks #
noncomputable def
Verification.joinKernel
(C D : ProbabilityTheory.Copula 2)
(a v u : ↑unitInterval)
:
Equations
- Verification.joinKernel C D a v u = Verification.unitJoin a (fun (t : ↑unitInterval) => C.conditionalCDF t v) (fun (t : ↑unitInterval) => D.conditionalCDF t v) u
Instances For
theorem
Verification.joinKernel_measurable
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
:
Measurable fun (p : ↑unitInterval × ↑unitInterval) => joinKernel C D a p.1 p.2
theorem
Verification.joinKernel_integrable
(C D : ProbabilityTheory.Copula 2)
(a v : ↑unitInterval)
:
theorem
Verification.joinKernel_mean
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
(v : ↑unitInterval)
:
theorem
Verification.joinKernel_mono
(C D : ProbabilityTheory.Copula 2)
(a u : ↑unitInterval)
:
Monotone fun (v : ↑unitInterval) => joinKernel C D a v u
theorem
Verification.joinKernel_zero
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
:
joinKernel C D a 0 =ᵐ[MeasureTheory.volume] fun (x : ↑unitInterval) => 0
theorem
Verification.joinKernel_one
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
:
joinKernel C D a 1 =ᵐ[MeasureTheory.volume] fun (x : ↑unitInterval) => 1
noncomputable def
Verification.conditionalJoin
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
:
Equations
- Verification.conditionalJoin C D a ha0 ha1 = Verification.copulaOfConditionalAE (Verification.joinKernel C D a) ⋯ ⋯ ⋯ ⋯ ⋯
Instances For
theorem
Verification.conditionalJoin_conditionalCDF
(C D : ProbabilityTheory.Copula 2)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
(v : ↑unitInterval)
:
(fun (u : ↑unitInterval) => (conditionalJoin C D a ha0 ha1).conditionalCDF u v) =ᵐ[MeasureTheory.volume]
joinKernel C D a v