openAI-math-dataset / lean /OAI /Probability /FreeIsing /Brownian /ContinuousIntegral.lean
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import OAI.Probability.FreeIsing.Brownian.IntegralContinuitySupport
import OAI.Probability.FreeIsing.Martingales.L2Closure
namespace OAI
noncomputable section
open MeasureTheory ProbabilityTheory Filter Set
open scoped NNReal Topology
namespace Problem367.BrownianIntegralProcess
variable {Ω : Type*} [mΩ : MeasurableSpace Ω] {P : Measure Ω}
omit mΩ in
theorem stoppedUniformSum_terminal (B H : ℝ≥0 → Ω → ℝ) (T : ℝ≥0) (n : ℕ) :
stoppedUniformSum B H T n T = BrownianGrid.uniformSum B H T n := by
funext ω
unfold stoppedUniformSum BrownianGrid.uniformSum BrownianGrid.integralSum
apply Finset.sum_congr rfl
intro i hi
have hin : i < n := Finset.mem_range.mp hi
have hn : 0 < n := lt_of_le_of_lt (Nat.zero_le i) hin
have ha := BrownianGrid.uniformGrid_le_horizon T hn hin.le
have hb := BrownianGrid.uniformGrid_le_horizon T hn hin
simp only [StoppedSimpleIntegral.intervalSum, min_eq_right ha, min_eq_right hb]
theorem integral_sq_sub_tendsto_of_L2_tendsto
{X : ℕ → Ω → ℝ} {L : Ω → ℝ}
(hX : ∀ n, MemLp (X n) 2 P) (hL : MemLp L 2 P)
(hlim : Tendsto (fun n => (hX n).toLp (X n)) atTop (𝓝 (hL.toLp L))) :
Tendsto (fun n => ∫ ω, (X n ω - L ω) ^ 2 ∂P) atTop (𝓝 0) := by
have hdist := (tendsto_iff_dist_tendsto_zero.mp hlim).pow 2
simpa only [MartingaleSquareMaximal.dist_toLp_sq, zero_pow (by decide : 2 ≠ 0)] using hdist
theorem exists_continuous_stopped_uniform_integral
{B H : ℝ≥0 → Ω → ℝ} {F : Filtration ℝ≥0 mΩ}
(hB : IsPreBrownianReal B P) (hAd : StronglyAdapted F B)
(hind : ∀ s t, s ≤ t → Indep
(MeasurableSpace.comap (fun ω => B t ω - B s ω) inferInstance) (F s) P)
(hBc : ∀ᵐ ω ∂P, Continuous (fun t => B t ω))
(hH : StronglyAdapted F H)
(hc : ∀ᵐ ω ∂P, Continuous (fun t => H t ω))
{C : ℝ} (hC : 0 ≤ C) (hb : ∀ᵐ ω ∂P, ∀ t, |H t ω| ≤ C) (T : ℝ≥0) :
∃ I : ℝ≥0 → Ω → ℝ, IsStronglyPredictable F I ∧ Martingale I F P ∧
(∀ᵐ ω ∂P, Continuous (fun t => I t ω)) ∧
(∀ t, MemLp (I t) 2 P) ∧ I 0 =ᵐ[P] 0 ∧
(∀ t, (∫ ω, I t ω ∂P) = 0) ∧
(∀ᵐ ω ∂P, ∀ t, T ≤ t → I t ω = I T ω) ∧
(∫ ω, (I T ω) ^ 2 ∂P) =
(∫ t in (0 : ℝ)..(T : ℝ), ∫ ω, (H t.toNNReal ω) ^ 2 ∂P) ∧
(∀ t, Tendsto (fun n => ∫ ω,
(stoppedUniformSum B H T n t ω - I t ω) ^ 2 ∂P) atTop (𝓝 0)) := by
let : IsProbabilityMeasure P := hB.isGaussianProcess.isProbabilityMeasure
have hH2 (t : ℝ≥0) : MemLp (H t) 2 P := by
apply MemLp.of_bound ((hH t).mono (F.le t)).aestronglyMeasurable C
filter_upwards [hb] with ω hω
simpa only [Real.norm_eq_abs] using hω t
have hS2 (n : ℕ) (t : ℝ≥0) : MemLp (stoppedUniformSum B H T n t) 2 P :=
BrownianFiltration.stopped_predictable_sum_memLp_two hB hind
(BrownianGrid.uniformGrid T n) (BrownianGrid.uniformGrid_mono T n)
(fun i => H (BrownianGrid.uniformGrid T n i)) (fun i => hH _) (fun i => hH2 _)
(Finset.range n) t
have hSM (n : ℕ) : Martingale (stoppedUniformSum B H T n) F P := by
apply BrownianFiltration.stopped_predictable_sum_martingale hB hAd hind
(BrownianGrid.uniformGrid T n) (BrownianGrid.uniformGrid_mono T n)
(fun i => H (BrownianGrid.uniformGrid T n i)) (fun i => hH _) (fun _ => C)
(fun i => ?_) (Finset.range n)
filter_upwards [hb] with ω hω
simpa only [Real.norm_eq_abs] using hω (BrownianGrid.uniformGrid T n i)
have hSC : CauchySeq (fun n => (hS2 n T).toLp (stoppedUniformSum B H T n T)) := by
have hclass : (fun n => (hS2 n T).toLp (stoppedUniformSum B H T n T)) =
BrownianFiltrationGrid.uniformSumLp hB hAd hind H hH hH2 T := by
funext n
apply Lp.ext
exact (hS2 n T).coeFn_toLp.trans
((stoppedUniformSum_terminal B H T n).eventuallyEq.trans
(BrownianFiltrationGrid.coe_uniformSumLp hB hAd hind H hH hH2 T n).symm)
rw [hclass]
exact BrownianFiltrationGrid.cauchySeq_uniformSumLp hB hAd hind H hH hH2 T
(fun ε hε => MeanSquareContinuity.uniform_integral_sq_sub_le
(fun t => (hH2 t).aestronglyMeasurable) hc hC hb T hε)
obtain ⟨L, hLp, hLc, hLlim⟩ :=
MartingaleSquareMaximal.exists_predictable_continuous_L2_limit hSM hS2
(ae_continuous_stoppedUniformSum hBc T) T hSC
obtain ⟨M, hM, hM2, hMzero, hMstop, hMiso, hMlim⟩ :=
exists_stopped_uniform_integral_process hB hAd hind hH hc hC hb T
have hLM (t : ℝ≥0) (ht : t ≤ T) : L t =ᵐ[P] M t := by
obtain ⟨hL2, hlim⟩ := hLlim t ht
exact BrownianGrid.ae_eq_of_mean_square_limits (fun n => hS2 n t) hL2 (hM2 t)
(integral_sq_sub_tendsto_of_L2_tendsto (fun n => hS2 n t) hL2 hlim) (hMlim t)
let R : ℝ≥0 → Ω → ℝ := fun t => L (min t T)
have hRad : StronglyAdapted F R := fun t =>
(hLp.stronglyAdapted (min t T)).mono (F.mono (min_le_left _ _))
have hRc : ∀ᵐ ω ∂P, Continuous (fun t => R t ω) := by
filter_upwards [hLc] with ω hω
exact hω.comp_continuous (continuous_id.min continuous_const) (fun t => min_le_right t T)
let I := PredictableMesh.repair R
have hIp : IsStronglyPredictable F I := PredictableMesh.repair_isStronglyPredictable hRad
have hIR : ∀ᵐ ω ∂P, ∀ t, I t ω = R t ω := by
filter_upwards [hRc] with ω hω
intro t
exact PredictableMesh.repair_eq_of_continuousOn (T := t) hω.continuousOn le_rfl
have hIM (t : ℝ≥0) : I t =ᵐ[P] M t := by
filter_upwards [hIR, hLM (min t T) (min_le_right _ _)] with ω hIω hω
rw [hIω t]
change L (min t T) ω = M t ω
rw [hω]
by_cases ht : t ≤ T
· rw [min_eq_left ht]
· rw [min_eq_right (le_of_not_ge ht), hMstop t (le_of_not_ge ht)]
refine ⟨I, hIp, hM.congr hIp.stronglyAdapted (fun t => (hIM t).symm), ?_,
(fun t => (hM2 t).ae_eq (hIM t).symm), ?_, ?_, ?_, ?_, ?_⟩
· filter_upwards [hIR, hRc] with ω hIω hω
exact hω.congr (fun t => (hIω t).symm)
· exact (hIM 0).trans (initial_zero_of_stopped_uniform_limit T (hM2 0) (hMlim 0))
· intro t
exact (integral_congr_ae (hIM t)).trans (hMzero t)
· filter_upwards [hIR] with ω hω
intro t ht
rw [hω t, hω T]
dsimp [R]
rw [min_eq_right ht, min_self]
· rw [← hMiso]
apply integral_congr_ae
filter_upwards [hIM T] with ω hω
rw [hω]
· intro t
apply (hMlim t).congr'
apply Filter.Eventually.of_forall
intro n
apply integral_congr_ae
filter_upwards [hIM t] with ω hω
rw [hω]
end Problem367.BrownianIntegralProcess
end
end OAI