Source-pinned research

Research proof index

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Project-declaredLean 4.33.0-rc1

Countable not cadlag Modif ae eq

ProbabilityTheory.countable_not_cadlagModif_ae_eq

Plain-language statement

The set of points where the cadlag modification of a real quasimartingale along a countable dense set T disagrees with X is countable.

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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Project-declaredLean 4.33.0-rc1

Countable not right Lim Within ae eq

ProbabilityTheory.countable_not_rightLimWithin_ae_eq

Plain-language statement

The set of points where the right limit along a countable dense set T disagrees with X is countable.

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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Project-declaredLean 4.33.0-rc1

Generate From eq predictable

ProbabilityTheory.ElementaryPredictableSet.generateFrom_eq_predictable

Plain-language statement

The elementary predictable sets generate the predictable σ-algebra. Note that we require the time domain to have countably generated atTop so that each (t, ∞] can be written as a countable union of intervals (t, s].

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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Project-declaredLean 4.33.0-rc1

Exists seq gt tendsto of not countable

ProbabilityTheory.exists_seq_gt_tendsto_of_not_countable

Plain-language statement

Any uncountable set in a separable, densely-ordered, first-countable linear order admits a strictly decreasing sequence of its elements converging to a point from the right.

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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Project-declaredLean 4.33.0-rc1

Has Indep Increments is Gaussian Process

ProbabilityTheory.HasIndepIncrements.isGaussianProcess

Plain-language statement

A stochastic process X with independent increments and such that X t is gaussian for all t is a Gaussian process.

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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Project-declaredLean 4.33.0-rc1

Integral sum weight increments mem Icc

ProbabilityTheory.integral_sum_weight_increments_mem_Icc

Plain-language statement

Two-sided expectation bound for adapted {0,1}-weighted increment sums of X, from the boundedness of elementary stochastic integrals at time t. The lower bound uses the complementary weights 1 - W.

probabilitystochastic processesmeasure theory

Source project: Brownian motion

Person-level attribution pending.

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