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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Invariant mass and almost-sure hitting of accessible sets
Statement
Let have invariant probability . If a measurable set has positive probability of being hit from every starting state, then for -almost every . In addition, if is -irreducible and , then and the positive-hitting hypothesis holds.
Facts & Assumptions
Given: A probability kernel with invariant probability; for the first conclusion, the positive-hitting premise; for the additional conclusion, a -irreducibility measure and .
Phi-irreducibility for a general-state Markov kernel: every -positive set is reached with positive probability from every state at some finite positive time.
Canonical Markov chain on path space: under its stated AC convention, iterated kernels define the canonical path law.
Discrete strong Markov property: the canonical chain has the strong Markov property at discrete stopping times.
Monotone convergence for the integral: increasing countable sums and limits pass through integration.
Tonelli's theorem for nonnegative measurable functions on a sigma-finite product: nonnegative sums and kernel integrals may be interchanged.
Measurability of integration against a kernel: integrating a bounded measurable function against a probability kernel gives a measurable function of the starting state.
Assume The Axiom of Choice as required by the canonical path-law [F3] and strong Markov [F4] suppliers. The countable level-set arguments below need no measurable selection theorem.
Proof
Given: The invariant law and applicable hypotheses, with [F1]–[F7] and [A1].
For the additional conclusion, let . By [F1], everywhere. Tonelli and stationarity give . If , this integral is zero, contradicting positivity of under a probability measure. Hence ; [F1] also supplies the positive-hitting premise for the first assertion.
It remains to prove the first assertion under its positive-hitting premise. Set and recursively , where . Then ; induction and [F7] show each is measurable, and , so is measurable. If the bad set has positive mass, a countable level-set union gives some for which has positive -measure. For each , and , so some has . Countable unions over times and rational thresholds then give a common , , and with and on .
Let . At each such visit, the conditional probability of avoiding at the next -step time is at most . Induction using [F4] gives for each finite , and hence . Thus for , . To select a measurable positive-mass set with a common finite last-visit bound, for each forbid at every time and also forbid at times with . For a finite horizon , let if for some , and otherwise. Set and recursively set for . Then is the finite-horizon event probability and is measurable by [F7]. As , these events decrease to , so continuity from above makes their limit measurable. The events increase with and their union is , so a value at least on . A countable rational threshold selection therefore gives , , and with and
Put and , with if no such visit occurs. For , consider the event that , there is no later -visit at a positive multiple of , and no hit of at or after . It is a subset of . Stationarity and the Markov property at time therefore give Indeed the event in step 2.1 rules out every -visit at skeleton indices , hence every later -visit at a positive multiple of , as well as every future hit of . The events are disjoint, so stationarity yields . Taking contradicts . Therefore the bad set is -null.
Depends on
- Canonical Markov chain on path space
- The Axiom of Choice
- Invariant and stationary distribution for a Markov kernel
- Phi-irreducibility for a general-state Markov kernel
- Measurability of integration against a kernel
- Discrete strong Markov property
- Monotone convergence for the integral
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
- A petite drift enlargement is small under aperiodicity Lemma
- Geometric drift yields a bounded small set and bivariate drift Lemma
- Small-set coupling yields almost-everywhere total-variation convergence Lemma
- General-state Markov-chain convergence under recurrence conditions Theorem
- Geometric ergodicity under drift and small-set minorization Theorem
Dependency tree · two levels
42 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)