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.
AC supplies countable selections and prescribed serial paths
Statement
Assume AC. Then countable choice holds. Moreover, if is serial on a nonempty set X and , there is a sequence with and for every n. Thus the countable and dependent choices required by the probability-product suppliers are available under AC.
Facts & Assumptions
AC supplies a choice function on a set of nonempty sets. The Axiom of Choice.
Countable choice is selection from an omega-indexed nonempty family. The Axiom of Countable Choice ().
DC requires a serial path starting at a prescribed point. The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain.
A given self-map and initial point have a uniquely specified natural-number iterate sequence. The recursion theorem.
Proof
Given: Assume AC. Then countable choice holds. Moreover, if is serial on a nonempty set X and , there is a sequence with and for every n. Thus the countable and dependent choices required by the probability-product suppliers are available under AC.
For an omega-indexed family of nonempty sets, its image is a set of nonempty sets. By [F1] choose with for every . Then is a function and , which is [F2]. Repeated sets use the same selected value and cause no ambiguity. For an empty index family the empty function already suffices; singleton fibers force their unique value.
For the stated serial R, every fiber is nonempty. AC applied to the set of all these fibers gives c with . Define the self-map on X. No recursively changing choice is being assumed: s is now one fixed function chosen from a fixed set of nonempty fibers. It satisfies for every x.
Apply [F4] to X, the prescribed a and s to obtain and for every natural n. The defining property of s gives , precisely [F3]. If X has one element and R is serial, this is its constant sequence. Empty X is excluded by the prescribed a. The uses of AC are exactly the two fixed-family selections in steps 1.1 and 1.2; recursion itself requires no further choice. This proves the two implications from AC, not either converse.
Depends on
Used by
- Cadlag Brownian-filtration local martingales have continuous versions Corollary
- De Moivre-Laplace central limit theorem Corollary
- Deterministic Ito integrals are Gaussian Corollary
- Existence and scaling of d-dimensional Brownian motion Corollary
- Finite-dimensional representations separate points Corollary
- Heat-semigroup martingales Corollary
- Proper full conditionals of a nondegenerate bivariate normal law Corollary
- Square-integrable Brownian terminal variables have Ito representations Corollary
- The Brownian square martingale Corollary
- The exponential Brownian martingale Corollary
- Vector Levy characterization Corollary
- A nonadapted step integrand breaks the Ito isometry Counterexample
- An unbounded stopped exponential martingale needs uniform integrability Counterexample
- Bounded-variation Riemann-Stieltjes theory does not construct the Brownian Ito integral Counterexample
- Infinite variance can defeat square-root-n CLT scaling Counterexample
- Product-measure equality is not pointwise equality Counterexample
- The ordinary chain rule fails for Brownian motion Counterexample
- Continuous Brownian Ito processes Definition
- Continuous-time adapted processes and martingales Definition
- Elementary predictable Brownian integrands Definition
- Empirical-Bayes credible-ball procedure in the inverse Gaussian sequence model Definition
- Hamiltonian Monte Carlo Definition
- Ito integral for square-integrable predictable processes Definition
- Ito integral of an elementary predictable process Definition
- Locally square-integrable predictable Brownian integrands Definition
- Multivariate normal law, including singular covariance Definition
- Natural and usual augmented Brownian filtrations Definition
- The Brownian differential generator Definition
- The iid multivariate-normal sample and covariance conventions Definition
- Wishart law by Gaussian outer products Definition
- A deterministic initial law is singular although the SDE is well-posed Example
- A deterministic step integrand Example
- A deterministic time-changed quadratic variation Example
- A Gaussian harmonic leapfrog resonance Example
- A globally Lipschitz Hamiltonian force Example
- A Lindeberg array with no identically distributed row Example
- A scalar Gaussian state-space update by completing the square Example
- CLT for sums of uniform random variables Example
- Covariance of deterministic Ito integrals Example
- Expected exit time from an interval Example
…and 56 more results.
Dependency tree · two levels
17 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
- Jech, The Axiom of Choice, §2.4, pp22–23; elementary AC-to-DC restriction proof (standard reference, not scraped)