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 two-valued random variable attains equality in Markov's inequality
Example
Let and . On a two-outcome finite probability space, give an event probability and define on and off . Then equality holds in Markov's inequality at threshold .
Facts & Assumptions
Given: Parameters , , and the construction in the Example.
Zero outcome weights are permitted in a finite probability space (Finite probability spaces, outcome weights, events, and event probabilities).
Real random variables and expectation are finite functions and weighted sums (Real random variables on finite probability spaces and their finite distributions, Expectation of a real random variable on a finite probability space).
Markov gives for nonnegative and (Markov's inequality on a finite probability space).
Verification
Give the two outcomes weights and ; these are nonnegative and sum to , including at .
The variable is nonnegative, , and has probability .
Hence , so [L3] is sharp.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
11 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
- H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes, Section 6.2.2 (standard reference, not scraped)