production module
AutoSamplingTheory.TechnicalLemmas.Probability.FiniteProductPairMarginal
2 named declarations scanned from AutoSamplingTheory/TechnicalLemmas/Probability/FiniteProductPairMarginal.lean.
Declarations
theorem AutoSamplingTheory.TechnicalLemmas.Probability.FiniteProductPairMarginal.map_pair_eval_eq_prod Partial Not mapped
- Distinct coordinate projections of a finite product probability measure have joint law equal to the product of their coordinate laws.
theorem map_pair_eval_eq_prod
(mu : I → ProbabilityMeasure X) {i j : I} (hij : i ≠ j) :
(Measure.pi (fun k => (mu k : Measure X))).map
(fun x : I → X => (x i, x j)) =
(mu i : Measure X).prod (mu j : Measure X) := by
have hind : iIndepFun (fun k (x : I → X) => x k)
(Measure.pi (fun k => (mu k : Measure X))) := by
simpa using
(iIndepFun_pi (μ := fun k => (mu k : Measure X))
(X := fun _k (x : X) => x) (fun _k => aemeasurable_id))
have hi : Measurable (fun x : I → X => x i) := measurable_pi_apply i
have hj : Measurable (fun x : I → X => x j) := measurable_pi_apply j
have hpair := (hind.indepFun hij).map_prod_eq_prod_map_map
hi.aemeasurable hj.aemeasurable
rw [(measurePreserving_eval (fun k => (mu k : Measure X)) i).map_eq,
(measurePreserving_eval (fun k => (mu k : Measure X)) j).map_eq] at hpair
exact hpair
/-- Integral form: an integrand depending on two distinct coordinates can be
integrated against the corresponding two-coordinate product law. -/
AutoSamplingTheory/TechnicalLemmas/Probability/FiniteProductPairMarginal.lean:29published source at 0e31a3cda412
theorem AutoSamplingTheory.TechnicalLemmas.Probability.FiniteProductPairMarginal.integral_comp_pair_eval Partial Not mapped
- Integral form: an integrand depending on two distinct coordinates can be integrated against the corresponding two-coordinate product law.
theorem integral_comp_pair_eval
(mu : I → ProbabilityMeasure X) {i j : I} (hij : i ≠ j)
{f : X × X → ℝ}
(hf : AEStronglyMeasurable f
((mu i : Measure X).prod (mu j : Measure X))) :
(∫ x : I → X, f (x i, x j)
∂Measure.pi (fun k => (mu k : Measure X))) =
∫ z, f z ∂((mu i : Measure X).prod (mu j : Measure X)) := by
have hmap := map_pair_eval_eq_prod mu hij
rw [← hmap, integral_map]
· exact ((measurable_pi_apply i).prodMk (measurable_pi_apply j)).aemeasurable
· rwa [hmap]
end
end FiniteProductPairMarginal
end Probability
end TechnicalLemmas
end AutoSamplingTheory
AutoSamplingTheory/TechnicalLemmas/Probability/FiniteProductPairMarginal.lean:49published source at 0e31a3cda412