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.
An invariant Gibbs target can still give reducible dynamics
Statement
Let the target on be , with the exact single-coordinate full conditionals. The random-scan Gibbs kernel leaves invariant but is reducible.
Facts & Assumptions
Given: The finite product state space, target and exact block-update kernels in the Statement.
Proper full conditional kernel: a full conditional is a pointwise probability kernel satisfying the target's conditional event identity.
Exact Gibbs block update: a block update retains the other coordinate and samples the selected coordinate from its full conditional.
Random and systematic Gibbs scan schemes: random scan chooses one of the two coordinate updates with fixed probability each.
Random-scan Gibbs is reversible and invariant: a finite mixture of exact Gibbs coordinate kernels is invariant for the common target.
Reducibility of a finite Markov chain: a finite chain is reducible when it has a nonempty proper closed subset.
Verification
Given: The target and conditional updates in the Statement and facts [F1]–[F5].
Each coordinate marginal under is uniform on . Given , the conditional law of is the point mass ; given , the conditional law of is . These are valid full conditionals because the target assigns mass only to the two diagonal points.
From either coordinate update leaves the state at ; from either update leaves it at . The random-scan mixture in [F3] therefore makes each singleton a closed class. The target is invariant by [F4], but is nonempty and proper, so reducibility follows from [F5].
Starting from , the time- law is , whose total-variation distance from is . Thus target invariance alone does not imply convergence from every start, uniqueness of the invariant law, or irreducibility.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
21 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
- Gareth O. Roberts and Jeffrey S. Rosenthal, General State Space Markov Chains and MCMC Algorithms (standard reference, not scraped)
- Randal Douc, Eric Moulines, Pierre Priouret, and Philippe Soulier, Markov Chains (standard reference, not scraped)