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Atlas.TheoryOfProbability.code.OptionalStoppingExpectation

theorem MeasureTheory.Submartingale.expected_stoppedValue_mono_real {Ω : Type u_1} {m0 : MeasurableSpace Ω} {μ : Measure Ω} {𝒢 : Filtration m0} {f : Ω} {τ π : Ωℕ∞} [SigmaFiniteFiltration μ 𝒢] (hf : Submartingale f 𝒢 μ) ( : IsStoppingTime 𝒢 τ) ( : IsStoppingTime 𝒢 π) (hle : τ π) {N : } (hbdd : ∀ (ω : Ω), π ω N) :
(x : Ω), stoppedValue f τ x μ (x : Ω), stoppedValue f π x μ

Optional stopping inequality for submartingales: if τ ≤ π are stopping times with π bounded by some constant N, then E[f_τ] ≤ E[f_π]. This is the monotonicity of expected stopped values along bounded stopping times.

theorem MeasureTheory.Supermartingale.expected_stoppedValue_le {Ω : Type u_1} {m0 : MeasurableSpace Ω} {μ : Measure Ω} {𝒢 : Filtration m0} {f : Ω} {τ : Ωℕ∞} [SigmaFiniteFiltration μ 𝒢] (hf : Supermartingale f 𝒢 μ) ( : IsStoppingTime 𝒢 τ) {N : } (hbdd : ∀ (ω : Ω), τ ω N) :
(x : Ω), stoppedValue f τ x μ (x : Ω), f 0 x μ

Optional stopping inequality for supermartingales: if τ is a bounded stopping time and f is a supermartingale, then E[f_τ] ≤ E[f_0]. Dual of the submartingale version, obtained by negating.