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.
Uniform ergodicity gives no uniform practical mixing time
Statement
For every , the two-state kernel is uniformly geometrically ergodic, yet the family has no common finite practical mixing time as .
Facts & Assumptions
Given: The parameter range and transition matrix in the Statement.
Aperiodicity for a general-state Markov kernel: positive self-loops rule out a cyclic decomposition.
Total variation distance for probability laws: total variation is the supremum of event-probability differences.
Whole-space minorization gives uniform geometric ergodicity: a whole-space minorization yields a uniform geometric bound.
Phi-irreducibility for a general-state Markov kernel: each positive counting-measure set is accessible from every state.
Verification
Given: The matrix and facts [F1]–[F3].
The invariant law is by symmetry, and each state has positive holding probability . Every entry is positive, so it is irreducible by [F4], and each state has positive holding probability, so it is aperiodic. Moreover every row dominates , because its two entries are at least . This is a whole-space minorization with , .
The theorem [F3] gives , so each fixed chain is uniformly geometrically ergodic.
The nonconstant eigenvector has eigenvalue . Direct multiplication, or induction, gives For every fixed this tends to as . Hence no finite makes the distance smaller than (for example) uniformly over the family. Each chain is uniformly ergodic, but the mixing time depends on .
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
12 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)