How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Brownian motion
Definition
Assume the Axiom of Choice The Axiom of Choice. A real process is a standard Brownian motion if:
- almost surely;
- for every finite list , the increments are mutually independent and have laws ; and
- there is one measurable event with such that is continuous on for every .
By Brownian covariance is equivalent to independent stationary normal increments, the first two clauses are equivalent to saying that is a centered Gaussian process with covariance . Clause 3 is additional: it cannot be recovered from finite-dimensional distributions alone.
No filtration is part of this definition. In particular, no completed or right-continuous filtration and no Markov or martingale assertion is silently imposed. Modifications and indistinguishability retain the distinct meanings in Process law, modification, and indistinguishability. The cases , , and zero-length increments are respectively vacuous or already covered by ; the increment list itself is strictly increasing.
Choice is declared because the normal-law and Gaussian/increment-equivalence interfaces construct and identify normal laws under AC. The continuity clause selects no path and makes no additional use of choice.
Source notes
Sousi, Section 6.1 (printed p. 51), gives these three defining clauses. The common full-measure event formulation makes the pathwise quantifier explicit.
Depends on
Used by
- Brownian paths are locally Holder below one half Corollary
- d-dimensional Brownian motion Definition
- Wiener measure on continuous path space Definition
- A deterministic integral construction of a Gaussian process Example
- Brownian bridge from Brownian motion Example
- Brownian finite-dimensional density Example
- Covariance of overlapping Brownian increments Example
- Linear combinations of Brownian values are Gaussian Example
- Brownian scaling Theorem
- Brownian time inversion Theorem
- Existence of continuous Brownian motion Theorem
- Uniqueness of Wiener measure Theorem
Dependency tree · two levels
15 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Perla Sousi, Advanced Probability, Section 6.1 (standard reference, not scraped)