Plain-English statement
- The second moment of a Brownian increment is its elapsed time.
Read the mathematics first, then descend into Lean
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Proof architecture
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How to read the exact Lean declaration
Read a Lean theorem left-to-right exactly as you would unpack a mathematical sentence: name → ambient types → automatically inferred structures → explicit hypotheses → conclusion → proof. Then read the proof top-to-bottom as transformations of the current goal.
- NameWhat reusable mathematical fact is being created?
- ParametersWhich symbols are arbitrary, and which structures are inferred by typeclass search?
- PropositionAfter the colon, translate the Lean expression back into a paper statement.
- Proof actionsAfter
by, ask what each tactic does to the mathematical goal—not only what syntax it uses.
Syntax used on this page
This glossary is filtered to syntax that actually occurs in the declaration above. Open a symbol only when you meet it, rather than memorizing Lean grammar in advance.
Source voice and ASTIS voice stay separate
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Lean statement
theorem integral_increment_sq
(hB : IsBrownianMotionWithFiltration B filtration μ)
{s t : ℝ≥0} (hst : s ≤ t) :
∫ omega, (B t omega - B s omega) ^ 2 ∂μ = ((t - s : ℝ≥0) : ℝ) := by
let _ : IsProbabilityMeasure μ := hB.isProbabilityMeasure
have hpre := hB.isBrownian.toIsPreBrownianReal
have ht : MemLp (B t) 2 μ := hpre.isGaussianProcess.hasGaussianLaw_eval t |>.memLp_two
have hs : MemLp (B s) 2 μ := hpre.isGaussianProcess.hasGaussianLaw_eval s |>.memLp_two
have hmean : ∫ omega, (B t omega - B s omega) ∂μ = 0 :=
hB.integral_increment_eq_zero s t
have hvarT : Var[B t; μ] = (t : ℝ) := by
rw [← covariance_self ht.aemeasurable, hpre.covariance_eval, min_self]
have hvarS : Var[B s; μ] = (s : ℝ) := by
rw [← covariance_self hs.aemeasurable, hpre.covariance_eval, min_self]
have hcov : cov[B t, B s; μ] = (s : ℝ) := by
rw [hpre.covariance_eval, min_eq_right hst]
have hvar : Var[fun omega => B t omega - B s omega; μ] =
((t - s : ℝ≥0) : ℝ) := by
rw [variance_fun_sub ht hs, hvarT, hvarS, hcov, NNReal.coe_sub hst]
ring
have hvarianceEq := variance_eq_sub (ht.sub hs)
have hmean' : ∫ omega, (B t - B s) omega ∂μ = 0 := by
simpa using hmean
rw [hmean'] at hvarianceEq
norm_num at hvarianceEq
have hsquare : ∫ omega, ((B t - B s) ^ 2) omega ∂μ =
((t - s : ℝ≥0) : ℝ) := hvarianceEq.symm.trans hvar
simpa using hsquare
/-- The conditional second moment of a future Brownian increment is its
elapsed time. -/
Open AutoSamplingTheory/TechnicalLemmas/StochasticProcesses/BrownianMotion.lean:130published source at 7bcd37294df1
Proof architecture
replace an increment-square expectation by its elapsed time
Lean proof walkthrough
- Read the quantified variables and typeclass brackets as part of the mathematical statement; inferred arguments are not missing assumptions.
- `have` creates a named intermediate mathematical fact.
- `rw` rewrites by an established identity.
- `simpa` closes the goal after a controlled simplification of a typed result.
Why the statement has this shape
The declaration is kept at the reusable level recorded by its Registry tags and direct consumers. Explicit measures, spaces, wrappers, and regularity hypotheses expose interfaces that paper notation often infers. A theorem card explains those interfaces but never widens the compiled statement.
Hidden assumptions and non-claims
- Measurability is represented explicitly or must be supplied by a dependency.
- Almost-everywhere hypotheses depend on the stated measure and representative.