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.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Continuous-time filtrations and all-pairs martingales
Definition
Assume the Axiom of Choice. On a probability space , a continuous-time filtration is a family of sub-sigma-algebras of such that whenever . A process is adapted when is measurable from to its state space at every .
For any process of random elements , its natural filtration is
The generated sigma-algebra exists by Nonempty intersections of sigma-algebras are sigma-algebras, so the generated sigma-algebra exists and is minimal. Its generator families are nested in , so is a filtration; every is -measurable, and minimality makes this the smallest filtration to which is adapted. No completion or right-continuous augmentation is included.
A real process is an all-pairs continuous-time martingale relative to when:
- is adapted;
- for every ; and
- for every ,
The equality in clause 3 is equality of the almost-everywhere classes in Conditional expectation as an ae class. At it is the known-variable identity. The word “continuous-time” specifies the index set; it does not assert path continuity. Likewise the definition imposes neither right continuity nor completeness on the filtration. AC is declared exactly because the library's conditional-expectation existence theorem uses it; the filtration, adaptation, and natural-filtration constructions make no choices.
Source notes
Sousi, Section 2 and Definition 2.1, printed pp. 13--14, gives natural filtrations, adaptation, integrability, and the all-pairs martingale identity in discrete time. Section 3.1, printed pp. 28 and 33, replaces the index set by , defines continuous-time filtrations and adaptation, and states that the martingale definition is unchanged. The nonaugmentation and almost-everywhere-class conventions are made explicit here to match the library's conditional-expectation interface.
Depends on
Used by
- Vector Levy characterization Corollary
- An unbounded stopped exponential martingale needs uniform integrability Counterexample
- Continuous Brownian Ito processes Definition
- Continuous-time adapted processes and martingales Definition
- Continuous-time stopping times and stopped sigma-algebras Definition
- Cox proportional-hazards model and partial likelihood Definition
- Natural and usual augmented Brownian filtrations Definition
- Progressively measurable and predictable processes Definition
- Simple counting-process intensity and compensator contract Definition
- Temporal point-process models and conditional intensity Definition
- Exponential martingale Brownian tail bound Example
- Hitting probabilities from an exponential martingale Example
- Radial second moment of multidimensional Brownian motion Example
- A continuous finite-variation martingale is constant Lemma
- Bounded stopping preserves càdlàg square-integrable martingales Lemma
- Brownian closed-set hitting times are stopping times Lemma
- Characteristic exponential for a continuous local martingale with deterministic clock Lemma
- Continuous adapted processes are measurable at bounded stopping times Lemma
- Elementary predictable counting-process integrals are martingales Lemma
- Event-weighted Ito isometry after a conditioning time Lemma
- Planar Brownian annular exit probability Lemma
- Brownian-filtration martingale representation Theorem
- Compensators, brackets, and jump-integral isometry for reaction counts Theorem
- Dynkin formula for bounded Brownian stopping Theorem
- Levy characterization of Brownian motion Theorem
- One-dimensional Ito formula Theorem
Dependency tree · two levels
16 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, Sections 2 and 3.1 (standard reference, not scraped)