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Verification.ConditionalJoin

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Copulas obtained by tagging two conditional models on predictor blocks #

noncomputable def Verification.joinKernel (C D : ProbabilityTheory.Copula 2) (a v u : ↑unitInterval) :
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    theorem Verification.joinKernel_mean (C D : ProbabilityTheory.Copula 2) (a : ↑unitInterval) (ha0 : 0 < a) (ha1 : a < 1) (v : ↑unitInterval) :
    ∫ (u : ↑unitInterval), joinKernel C D a v u = ↑v
    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
    noncomputable def Verification.conditionalJoin (C D : ProbabilityTheory.Copula 2) (a : ↑unitInterval) (ha0 : 0 < a) (ha1 : a < 1) :
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      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