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.
A finite periodic chain need not converge in total variation
Statement
The deterministic two-state chain that alternates is -irreducible for counting measure and has invariant law , but from either fixed starting state its laws do not converge in total variation to .
Facts & Assumptions
Given: The state space and transition matrix in the Statement.
Transition matrices and n-step probabilities: the rows specify the one-step probabilities and matrix powers specify .
Phi-irreducibility for a general-state Markov kernel: every positive- set must be reached at some finite positive time from every state.
Aperiodicity for a general-state Markov kernel: a period-two cyclic partition violates aperiodicity.
Total variation distance for probability laws: total variation is the supremum of event-probability differences.
Verification
Given: The alternating transition matrix and facts [F1]–[F4].
Let be counting measure. Every nonempty set is reachable from either state at a positive time of at most two steps, so [F2] holds. The uniform law is invariant because each column sum and row sum of the transition matrix equals one.
The partition , has positive invariant mass in both classes and transitions alternate deterministically, so it is a cyclic decomposition of period two under [F3].
From , for even and for odd . Taking event in [F4], the distance to equals for every . From the even and odd laws are interchanged, giving the same distance. Hence convergence from either fixed state fails despite irreducibility and invariance; an initial law equal to remains invariant.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
11 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)