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.
A discontinuous payoff with bias order one and variance order one-half
Example
Fix an integer , put , and choose . Let have density on , let be an independent Rademacher variable, and use the discontinuous payoff . The target law is that of , and for each the level law is that of . Couple adjacent level laws using the same pair within each coupling.
For each correction sample, draw a fresh independent pair with the stated law; within each positive-level correction use that same pair for the fine and coarse payoffs, and take all samples in distinct level blocks independently as required by the definition. This gives an infinite compatible family whose finite prefixes are MLMC hierarchies. The example has bias order and single-correction variance order for positive levels. The level-zero correction is bounded and , so enlarge the variance constant to include it. One sample costs , so it also satisfies the abstract theorem's upper work assumption . Substituting that conservative work rate into the theorem gives the guarantee ; the example does not claim this is a sharp cost for its constant-work evaluator.
Facts & Assumptions
Given: An integer , , the density and payoff above, independence of and , and the common-pair coupling across each adjacent level.
Geometric MLMC hierarchy and coupled corrections: adjacent corrections may use any joint law with the stated fine and coarse marginals; here the same supplies that coupling.
MLMC cost complexity under bias, variance, and work rates: with , , and work exponent , the guaranteed cost exponent is .
Verification
This is a hierarchy of the form in [F1]. The density is nonnegative on because , and its integral is . The target mean is . Writing and averaging over gives Thus , so the bias exponent is .
For , put and . The correction is when and , is when and , and is zero otherwise. Integrating the density on those two intervals and averaging over the two signs gives The level-0 correction is bounded, so it has finite variance as well.
The two signed crossing probabilities give . Since , . For , and The last inequality follows from for . Hence , which proves the exact order .
The sample evaluator uses a constant number of draws, so its cost is because . Substituting , , into [F2] gives the stated upper guarantee under that work-rate bound.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
3 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
- Michael B. Giles, Multilevel Monte Carlo Path Simulation, §3 (standard reference, not scraped)