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.
Finite coupling makes connectivity probability monotone
Statement
For fixed integer and , the connectivity probabilities satisfy . This statement concerns graph connectivity only.
Facts & Assumptions
Given: The objects and hypotheses of the Statement.
Goal: Prove the Statement.
Finite product spaces assign to a coordinate tuple the product of its coordinate weights (The finite product of finite probability spaces).
Event probabilities in a finite space are sums of nonnegative outcome weights (Finite probability spaces, outcome weights, events, and event probabilities).
The law has independent edge coordinates of inclusion probability (The Erdős-Rényi finite random graph G(n,p)).
Connectivity means that every pair of vertices is joined by a finite path (Connected graphs and connected components defined by the existence of vertex paths).
Proof
Give each possible edge a coordinate with three outcomes of respective weights , and use their finite product. These weights are nonnegative and sum to one. Include an edge in if its coordinate is , and in if its coordinate is or . Summing coordinate weights shows the marginals are respectively and ; summing product weights over each prescribed coordinate block factors, so the graph marginals are and .
On every outcome the two graphs have the same nonempty vertex set , and every edge of is an edge of . Each path of therefore remains a path of , so the event that is connected is contained in the event that is connected. For the product outcome weights , nonnegativity gives . By the graph marginals in step 1.1 this is the desired inequality, including all endpoint cases.
Depends on
Dependency tree · 2 levels
Each result is drawn at its shortest distance from this item. Arrows run from prerequisites to the items that use them; this item has a heavier outline. External source nodes link directly to the cited text. The view shows up to two dependency steps; open a prerequisite to continue.