openAI-math-dataset / lean /OAI /Probability /FreeIsing /Conditioning /ContinuousCopyImmersion.lean
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import OAI.Probability.FreeIsing.Conditioning.CopyImmersion
import OAI.Probability.FreeIsing.Martingales.Pushforward
import OAI.Probability.FreeIsing.Stochastic.ContinuousCopyBrownian
namespace OAI
noncomputable section
open MeasureTheory ProbabilityTheory Filter
open scoped NNReal
namespace Problem367.ContinuousCopyBrownian
open WienerSampler ContinuousObservationHistory ContinuousNoiseSplit
variable {ι : Type*}
def singleHistory (s : ℝ≥0) (q : (ι → Path) × (ι → Path)) :=
(stoppedArray s q.1, stoppedArray s q.2)
lemma measurable_singleHistory (s : ℝ≥0) : Measurable (singleHistory (ι := ι) s) :=
((measurable_stoppedArray s).comp measurable_fst).prodMk
((measurable_stoppedArray s).comp measurable_snd)
lemma singleHistory_singleHistory {s t : ℝ≥0} (hst : s ≤ t)
(q : (ι → Path) × (ι → Path)) : singleHistory s (singleHistory t q) = singleHistory s q := by
simp only [singleHistory, stoppedArray_stoppedArray hst]
def singleFiltration : Filtration ℝ≥0
(inferInstance : MeasurableSpace ((ι → Path) × (ι → Path))) where
seq s := MeasurableSpace.comap (singleHistory s) inferInstance
mono' := by
intro s t hst
have h := MeasurableSpace.comap_mono (g := singleHistory (ι := ι) t)
(measurable_singleHistory s).comap_le
simpa only [MeasurableSpace.comap_comp, Function.comp_def,
singleHistory_singleHistory hst] using h
le' s := (measurable_singleHistory s).comap_le
lemma measurable_noisePast_singleFiltration (s : ℝ≥0) :
Measurable[singleFiltration (ι := ι) s]
(fun q : (ι → Path) × (ι → Path) => stoppedArray s q.1) :=
measurable_fst.comp (comap_measurable (singleHistory s))
lemma joinedFiltration_eq_left (s : ℝ≥0) :
joinedFiltration (ι := ι) s =
(singleFiltration s).comap (fun q : Joint (ι := ι) => (q.1, q.2.1)) ⊔
MeasurableSpace.comap (fun q : Joint (ι := ι) => stoppedArray s q.2.2) inferInstance := by
change MeasurableSpace.comap
(fun q : Joint (ι := ι) =>
(stoppedArray s q.1, (stoppedArray s q.2.1, stoppedArray s q.2.2)))
((inferInstance : MeasurableSpace (ι → Path)).prod
((inferInstance : MeasurableSpace (ι → Path)).prod
(inferInstance : MeasurableSpace (ι → Path)))) = _
simp only [singleFiltration, singleHistory, MeasurableSpace.comap_comp,
Function.comp_def]
change MeasurableSpace.comap
(fun q : Joint (ι := ι) =>
(stoppedArray s q.1, (stoppedArray s q.2.1, stoppedArray s q.2.2)))
((inferInstance : MeasurableSpace (ι → Path)).prod
((inferInstance : MeasurableSpace (ι → Path)).prod
(inferInstance : MeasurableSpace (ι → Path)))) =
MeasurableSpace.comap
(fun q : Joint (ι := ι) => (stoppedArray s q.1, stoppedArray s q.2.1))
((inferInstance : MeasurableSpace (ι → Path)).prod
(inferInstance : MeasurableSpace (ι → Path))) ⊔
MeasurableSpace.comap (fun q : Joint (ι := ι) => stoppedArray s q.2.2) inferInstance
rw [MeasurableSpace.comap_prodMk, MeasurableSpace.comap_prodMk,
MeasurableSpace.comap_prodMk, sup_assoc]
theorem exists_past_kernel [Countable ι]
(ρ : Measure (ι → Path)) [IsProbabilityMeasure ρ]
(η : Kernel (ι → Path) (ι → Path)) [IsMarkovKernel η] (s : ℝ≥0)
(hind : IndepFun (singleHistory s)
(fun q : (ι → Path) × (ι → Path) => futureArray s q.1) (ρ ⊗ₘ η)) :
∃ κ : Kernel (ι → Path) (ι → Path), IsMarkovKernel κ ∧
(fun w => (η.map (stoppedArray s)) w) =ᵐ[ρ]
fun w => κ (stoppedArray s w) :=
ConditionalCopyImmersion.exists_past_kernel_of_reconstructible_split ρ
(stoppedArray s) (measurable_stoppedArray s)
(futureArray s) (measurable_futureArray s) (measurableEmbedding_split s)
η (stoppedArray s) (measurable_stoppedArray s) hind
theorem martingale_left_of_future_indep [Countable ι]
(ρ : Measure (ι → Path)) [IsProbabilityMeasure ρ]
(η : Kernel (ι → Path) (ι → Path)) [IsMarkovKernel η]
(hind : ∀ s, IndepFun (singleHistory s)
(fun q : (ι → Path) × (ι → Path) => futureArray s q.1) (ρ ⊗ₘ η))
{M : ℝ≥0 → (ι → Path) × (ι → Path) → ℝ}
(hM : Martingale M singleFiltration (ρ ⊗ₘ η)) :
Martingale (fun t (q : Joint (ι := ι)) => M t (q.1, q.2.1))
joinedFiltration (ρ ⊗ₘ (η ×ₖ η)) := by
exact ConditionalCopyImmersion.martingale_left ρ η
stoppedArray measurable_stoppedArray stoppedArray measurable_stoppedArray
singleFiltration joinedFiltration joinedFiltration_eq_left
measurable_noisePast_singleFiltration
(fun s => exists_past_kernel ρ η s (hind s)) hM
lemma measurable_swap_joinedFiltration (s : ℝ≥0) :
Measurable[joinedFiltration (ι := ι) s, joinedFiltration s]
(fun q : Joint (ι := ι) => (q.1, (q.2.2, q.2.1))) := by
apply Measurable.of_comap_le
change (MeasurableSpace.comap (history s)
(inferInstance : MeasurableSpace (Joint (ι := ι)))).comap
(fun q : Joint (ι := ι) => (q.1, (q.2.2, q.2.1))) ≤ _
rw [MeasurableSpace.comap_comp]
have hh : Measurable[joinedFiltration (ι := ι) s] (history s) := comap_measurable _
have hm := (measurable_fst.comp hh).prodMk
(((measurable_snd.comp measurable_snd).comp hh).prodMk
((measurable_fst.comp measurable_snd).comp hh))
exact hm.comap_le
theorem martingale_right_of_future_indep [Countable ι]
(ρ : Measure (ι → Path)) [IsProbabilityMeasure ρ]
(η : Kernel (ι → Path) (ι → Path)) [IsMarkovKernel η]
(hind : ∀ s, IndepFun (singleHistory s)
(fun q : (ι → Path) × (ι → Path) => futureArray s q.1) (ρ ⊗ₘ η))
{M : ℝ≥0 → (ι → Path) × (ι → Path) → ℝ}
(hM : Martingale M singleFiltration (ρ ⊗ₘ η)) :
Martingale (fun t (q : Joint (ι := ι)) => M t (q.1, q.2.2))
joinedFiltration (ρ ⊗ₘ (η ×ₖ η)) := by
have hl := martingale_left_of_future_indep ρ η hind hM
apply MartingalePushforward.martingale_of_pullback
(ConditionalCopyImmersion.swap_measurePreserving ρ η)
(F := joinedFiltration) (G := joinedFiltration)
(fun t => (measurable_swap_joinedFiltration t).comap_le)
(fun t => (hl.stronglyAdapted t).comp_measurable (measurable_swap_joinedFiltration t))
exact hl
end Problem367.ContinuousCopyBrownian
end
end OAI