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.
Recurrent and transient states
Definition
For a specified Markov chain law with transition matrix and deterministic initial state , write for that law. The state is recurrent when
and transient when
Here is the strictly positive return time of Hitting, return, and visit times, so the initial visit at time zero does not count as a return. Since the displayed return probability lies in , these alternatives exhaust all states. The definition concerns the specified law and does not assert that laws for every state can be selected simultaneously.
Depends on
Used by
- Higher-dimensional simple symmetric walks are transient Corollary
- Irreducible recurrence/transience dichotomy Corollary
- One-dimensional simple symmetric walk is recurrent Corollary
- Two-dimensional simple symmetric walk is recurrent Corollary
- A transient chain can return with positive probability Counterexample
- Different classes can have different recurrence types Counterexample
- Birth–death recurrence through scale products Example
- Communicating classes in a four-state chain Example
- Green kernel of a biased integer walk Example
- Geometric tail for hitting in a finite irreducible chain Lemma
- Equivalent criteria for recurrence and transience Theorem
- Recurrence and transience are class properties Theorem
- Renewal decomposition at successive returns Theorem
Dependency tree · two levels
6 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
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Levin, Peres and Wilmer, Markov Chains and Mixing Times, second edition (standard reference, not scraped)