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 loaded die as a nonuniform finite probability space
Example
On assign weights This is a loaded die with a possible zero-weight outcome. Let and .
Facts & Assumptions
Given: The weights and events in the Example.
Finite probability spaces allow nonnegative outcome weights summing to (Finite probability spaces, outcome weights, events, and event probabilities).
Complements and differences obey the finite probability laws (Normalization, nonnegativity, monotonicity, complements, and differences in a finite probability space).
when (Conditional probability for ).
The multiplication rule is (The multiplication rule and finite chain rule for conditional probability).
Verification
The six weights sum to , so [L1] gives a finite probability space; is nonempty but has probability zero.
Direct summation gives , , and .
Since , [L3] gives .
Finally , verifying [L4] in this nonuniform space.
Depends on
- Finite probability spaces, outcome weights, events, and event probabilities
- Normalization, nonnegativity, monotonicity, complements, and differences in a finite probability space
- Conditional probability $\mathbb P(A\mid B)$ for $\mathbb P(B)>0$
- The multiplication rule and finite chain rule for conditional probability
Used by
Nothing in the library uses this result yet.
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 39 results over 12 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- C. M. Grinstead and J. L. Snell, Introduction to Probability, 2nd ed., Section 1.2 (standard reference, not scraped)