import Mathlib.MeasureTheory.Constructions.Pi import Mathlib.MeasureTheory.Integral.Prod import Mathlib.MeasureTheory.Integral.Bochner.SumMeasure namespace OAI /-! # Precommitted finite conditional-kernel tables The secretary reconstruction may be implemented with no randomness drawn after arrivals: draw one independent kernel output for every possible finite observation before arrivals, and later read only the entry indexed by the observation. The entire table, including unused entries, is part of the initial seed. These lemmas establish the law of that concrete construction. They do not assert independence after conditioning on the entire table, and do not assume a secretary transport theorem or a reward guarantee. -/ namespace MatroidProphet.PrecommittedKernel open MeasureTheory variable {I A : Type*} [Fintype I] [Fintype A] [MeasurableSpace I] [MeasurableSingletonClass I] [MeasurableSpace A] [MeasurableSingletonClass A] omit [Fintype A] [MeasurableSpace I] [MeasurableSingletonClass I] [MeasurableSingletonClass A] in /-- Reading one fixed coordinate from the initial table has the prescribed law. -/ theorem map_eval (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] (i : I) : Measure.map (fun q : I → A => q i) (Measure.pi κ) = κ i := (measurePreserving_eval κ i).map_eq omit [MeasurableSpace I] [MeasurableSingletonClass I] in /-- Every statistic of one kernel output has the same expectation under the precommitted table construction. No later draw is needed. -/ theorem integral_eval (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] (i : I) (f : A → ℝ) : (∫ q : I → A, f (q i) ∂Measure.pi κ) = ∫ a, f a ∂κ i := by have h := integral_map (μ := Measure.pi κ) (measurable_pi_apply i).aemeasurable (measurable_of_countable f).aestronglyMeasurable rw [map_eval κ i] at h exact h.symm /-- An observation independent of the whole initial table selects exactly its conditional kernel. The function may depend jointly on observation and output. -/ theorem integral_joint (μ : Measure I) [IsFiniteMeasure μ] (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] (f : I → A → ℝ) : (∫ z : I × (I → A), f z.1 (z.2 z.1) ∂μ.prod (Measure.pi κ)) = ∫ i, ∫ a, f i a ∂κ i ∂μ := by rw [integral_prod _ Integrable.of_finite] apply integral_congr_ae exact Filter.Eventually.of_forall fun i => integral_eval κ i (f i) /-- Exact joint atom law, retaining the observation as well as reconstructed output. This is stronger than only identifying the output marginal. -/ theorem joint_singleton (μ : Measure I) [IsFiniteMeasure μ] (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] (i : I) (a : A) : Measure.map (fun z : I × (I → A) => (z.1, z.2 z.1)) (μ.prod (Measure.pi κ)) {(i, a)} = μ {i} * κ i {a} := by rw [Measure.map_apply (measurable_of_countable _) (measurableSet_singleton _)] have hpre : (fun z : I × (I → A) => (z.1, z.2 z.1)) ⁻¹' {(i, a)} = {i} ×ˢ ((fun q : I → A => q i) ⁻¹' {a}) := by ext z simp only [Set.mem_preimage, Set.mem_singleton_iff, Prod.mk.injEq, Set.mem_prod] constructor · rintro ⟨hi, ha⟩ exact ⟨hi, by simpa [hi] using ha⟩ · rintro ⟨hi, ha⟩ exact ⟨hi, by simpa [hi] using ha⟩ rw [hpre, Measure.prod_prod] congr 1 have h := congrArg (fun ν : Measure A => ν {a}) (map_eval κ i) rw [Measure.map_apply (measurable_pi_apply i) (measurableSet_singleton a)] at h exact h /-- A candidate joint law is recovered exactly if its atoms have the specified observation marginal times conditional kernel law. All unused table coordinates remain present in the concrete initial probability space. -/ theorem map_joint_eq (μ : Measure I) [IsFiniteMeasure μ] (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] (ν : Measure (I × A)) (hν : ∀ i a, ν {(i, a)} = μ {i} * κ i {a}) : Measure.map (fun z : I × (I → A) => (z.1, z.2 z.1)) (μ.prod (Measure.pi κ)) = ν := by apply Measure.ext_of_singleton rintro ⟨i, a⟩ exact (joint_singleton μ κ i a).trans (hν i a).symm /-- The observation may itself be any measurable function of a separate random experiment (for example a binomial cutoff and uniform arrival permutation). Only its marginal law matters to the precommitted-table reconstruction. -/ theorem map_random_index {Ω : Type*} [MeasurableSpace Ω] (μ : Measure Ω) [SFinite μ] (X : Ω → I) (hX : Measurable X) (κ : I → Measure A) [∀ i, IsProbabilityMeasure (κ i)] : Measure.map (fun z : Ω × (I → A) => (X z.1, z.2 (X z.1))) (μ.prod (Measure.pi κ)) = Measure.map (fun z : I × (I → A) => (z.1, z.2 z.1)) ((Measure.map X μ).prod (Measure.pi κ)) := by have hp := Measure.map_prod_map μ (Measure.pi κ) hX measurable_id rw [Measure.map_id] at hp rw [hp, Measure.map_map (measurable_of_countable _) (hX.prodMap measurable_id)] rfl section Dependent variable {B : I → Type*} [∀ i, Fintype (B i)] [∀ i, MeasurableSpace (B i)] [∀ i, MeasurableSingletonClass (B i)] omit [MeasurableSpace I] [MeasurableSingletonClass I] in /-- Dependent kernel tables allow the output type at each observation to be its valid fiber. Thus every initial table, including null-probability tables, obeys the reconstruction constraint by type, rather than only almost surely. -/ theorem integral_eval_dependent (κ : (i : I) → Measure (B i)) [∀ i, IsProbabilityMeasure (κ i)] (i : I) (f : B i → ℝ) : (∫ q : (i : I) → B i, f (q i) ∂Measure.pi κ) = ∫ a, f a ∂κ i := by have h := integral_map (μ := Measure.pi κ) (measurable_pi_apply i).aemeasurable (measurable_of_countable f).aestronglyMeasurable rw [(measurePreserving_eval κ i).map_eq] at h exact h.symm /-- Exact conditional expectation for dependent-fiber precommitted tables. -/ theorem integral_joint_dependent (μ : Measure I) [IsFiniteMeasure μ] (κ : (i : I) → Measure (B i)) [∀ i, IsProbabilityMeasure (κ i)] (f : (i : I) → B i → ℝ) : (∫ z : I × ((i : I) → B i), f z.1 (z.2 z.1) ∂μ.prod (Measure.pi κ)) = ∫ i, ∫ a, f i a ∂κ i ∂μ := by rw [integral_prod _ Integrable.of_finite] apply integral_congr_ae exact Filter.Eventually.of_forall fun i => integral_eval_dependent κ i (f i) end Dependent end MatroidProphet.PrecommittedKernel end OAI