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.
The finite union bound
Statement
For every finite family of events , For both sides are .
Facts & Assumptions
Given: A probability space and events , where is an integer.
A probability measure is a measure on its event sigma-algebra, with total mass one (Probability measures and probability spaces).
If is a measure and are measurable, then (Measures are monotone).
A measure assigns zero to the empty set and is countably additive on pairwise-disjoint measurable sequences (Measures on sigma-algebras).
A sigma-algebra is an algebra of subsets, so it contains the empty set and is closed under finite unions and differences (Sigma-algebras).
Proof
For , put . By [L4], every is measurable. They are pairwise disjoint and satisfy . Their union equals : a point in the latter has a least index at which it belongs to , and then belongs to ; the reverse inclusion follows from .
By [L1], is a measure. Extend the disjoint family by for . Countable additivity and from [L3] give . By [L2], each , so summing these finitely many inequalities proves .
If , the union is empty and the sum is the empty sum, so both sides are by [L3]. If , the bound is the equality . These arguments impose no finiteness assumption on .
Depends on
Used by
- Symmetric bounded-increment Azuma bound Corollary
- Weak tests against likelihood tilts do not ensure almost-sure posterior consistency Counterexample
- A parameter ledger for the high-girth, high-chromatic alteration proof Example
- The random-colouring proof of R(k,k)>2^k/2 Example
- The union bound can be strict for overlapping events Example
- A complete-or-weakly-sparse blockade can be thinned to equal subblocks with directional sparsity Lemma
- A low-error random code can be pruned to satisfy a strict average cost constraint Lemma
- Adaptive empirical-model accuracy for R-MAX Lemma
- Adjacent iid vector functionals admit a finite-moment strong approximation Lemma
- Bounded independent vector rows admit a logarithmic cubed Gaussian coupling Lemma
- Concentration for adaptively sampled MDP observations Lemma
- Delayed Q-learning optimism and update-count bounds Lemma
- Distance-Three exRec Correctness and Truncated Witness Reduction Lemma
- Gaussian vector norm and maximum-coordinate tails Lemma
- Heterogeneous bounded atom vector rows admit a logarithmic cubed Gaussian coupling Lemma
- ℙ(α(G(n,p))≥ s)leC(n, s)(1-p)^C(s, 2)≤ nˢ exp(-pC(s, 2)) for s≤ n Lemma
- Posterior-odds concentration in the Bernoulli model Lemma
- Signal alignment of a Gaussian sample mean Lemma
- Simultaneous initialization estimates for the Du architectures Lemma
- Squared-norm concentration for real and normalized complex Gaussians Lemma
- Tail bound for finite sums of independent signs Lemma
- Threshold decoding bound for memoryless channels with densities Lemma
- Uniform finite-horizon moments for tamed Euler approximations Lemma
- Uniform samples have logarithmic fill-distance rate on a cube Lemma
- Estimate an inner product from ℓ2-sampling access Proposition
- Adleman's theorem: BPP is contained in P/poly Theorem
- Concatenated Threshold Under Local-Stochastic Correlated Noise Theorem
- Conditional Concatenated Threshold for Proper Codes Theorem
- Conditional Distance-Three Extended-Rectangle Threshold Theorem
- For all positive k,ℓ, some finite graph has girth greater than ℓ and chromatic number greater than k Theorem
- For n≥1 independent random signs, ℙ(|S|≥ t)≤2 exp(-t²/(2n)) for t>0 Theorem
- Gaussian sketches embed a fixed subspace at the additive dimension rate Theorem
- If k≥1 and n≥3k² 2ᵏ, an n-vertex tournament with property Sₖ exists Theorem
- Planar Surface-Code Memory Bound Under IID Z Noise Theorem
Dependency tree · two levels
8 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, Lemma 1.1.3 (standard reference, not scraped)
- H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes, Section 6.2.1 (standard reference, not scraped)