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.
The first-moment method for avoiding or forcing a finite count of bad events
Statement
Let be a nonnegative integer-valued random variable on a finite probability space.
- If , some outcome has .
- If , some outcome has .
- More generally, some outcome satisfies and some satisfies .
Facts & Assumptions
Given: A nonnegative integer-valued random variable on a finite probability space.
Some outcome has value at least the expectation and some has value at most it (Expectation preserves pointwise order and lies between the minimum and maximum attained values).
Markov gives (Markov's inequality on a finite probability space).
An event of positive probability in a finite probability space is nonempty (An event of positive probability in a finite probability space is nonempty).
An event and its complement have probabilities summing to (Normalization, nonnegativity, monotonicity, complements, and differences in a finite probability space).
Proof
If , [L2] gives , so [L4] gives because a nonnegative integer is either zero or at least one.
If , an outcome with exists by [L1].
By [L3], the event is nonempty.
The two averaging assertions are exactly [L1]; steps 1.1 and 2.1 prove avoidance, and step 1.2 proves forcing.
Depends on
- Normalization, nonnegativity, monotonicity, complements, and differences in a finite probability space
- Expectation preserves pointwise order and lies between the minimum and maximum attained values
- Markov's inequality on a finite probability space
- An event of positive probability in a finite probability space is nonempty
Used by
- Expectation equal to 1 does not force a nonnegative integer-valued variable to vanish somewhere Counterexample
- Every k-uniform hypergraph with fewer than 2ᵏ⁻¹ edges is 2-colourable Theorem
- For every n≥16 there is an n-vertex graph with hom(G)<3 log₂ n Theorem
- Strong regularity with linearly large representative subsets and no irregular representative pair Theorem
Dependency tree · two levels
10 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
- J. Matousek and J. Vondrak, The Probabilistic Method, Chapter 2 (standard reference, not scraped)
- Y. Zhao, MIT 18.218 Probabilistic Method in Combinatorics, Section 2.1 (standard reference, not scraped)