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.
Geometric small-set renewal convergence gives an integrable exponential hitting moment
Statement
Assume the Axiom of Choice. Let be a probability kernel on any measurable space , with invariant probability . Let satisfy , and suppose where is a probability measure. Suppose some and satisfy For a -chain started at , put . Then some makes where . In particular is finite -almost everywhere; finiteness at every state is not asserted.
For the same hypotheses, the split construction also has the following return and occupation properties. Put and . Define , on , and off . On use the kernel and invariant law . Its atom has mass . If has law and , then some satisfies and for every nonnegative measurable on the split space, In particular almost surely and . These additional assertions use exactly the construction and hypotheses above; they do not assert an exponential entrance-time moment from every state.
Facts & Assumptions
Given: AC, the invariant law, small-set minorization, and scalar geometric-convergence bound in the Statement.
The Axiom of Choice: AC is assumed, as required by the path-space and Markov suppliers below.
Small-set minorization and geometric drift: the displayed minorization permits the probability residual on .
Canonical Markov chain on path space: probability kernels on arbitrary measurable spaces have canonical path laws under AC.
Measurability of integration against a kernel: integration of a nonnegative measurable function against a probability kernel is measurable.
Discrete strong Markov property: future bounded path functionals obey the Markov identity at stopping times.
Markov property for bounded future path functionals: bounded future-path expectations are measurable functions of the starting state and satisfy the deterministic-time Markov identity.
Invariant initial law makes a Markov chain stationary: an invariant initial distribution makes every finite-dimensional law stationary.
Monotone convergence for the integral: nonnegative increasing limits pass through integrals.
Tonelli's theorem for nonnegative measurable functions on a sigma-finite product: nonnegative double sums and probability integrals may be interchanged.
The sum of a complex power series is analytic throughout its open disc of convergence and Every complex analytic function is holomorphic: an absolutely convergent complex power series is holomorphic in its open disk of convergence.
A complex power-series sum has complex derivatives of every order, obtained by repeated termwise differentiation: power series can be differentiated repeatedly term by term inside their convergence disk.
All higher complex derivatives exist and satisfy Cauchy's integral formula on an interior circle: a holomorphic function has derivatives of every order locally; these derivatives are continuous because each has a further complex derivative.
Cauchy's inequalities bound the Taylor coefficients by the circle supremum: if on a radius- circle about contained with its disk in the holomorphic domain, then .
Invariant and stationary distribution for a Markov kernel: invariance is equality of the initial law with its one-step image.
Measure kernel and probability kernel: a probability kernel is measurable in its starting point and countably additive in its target event.
Proof
Given: The hypotheses in the Statement, with AC [A1].
Set and . On define ; define for , and otherwise. The split kernel is Nonnegativity follows from [F1], measurability from [F3], and finite summation over the two flag sections defines a probability on every product-measurable event. Thus [F2] realizes the split path. If the initial flag at has probabilities , summing it gives . The same summation at each subsequent time shows that the -coordinate path has the original -chain law. The set is an atom: all its states have the identical outgoing transition obtained by using .
Put . Summing its incoming transitions first over gives and then assigns the new flag according to , proving by [F13]. Let , and let , an ordinary normalized restriction. Write ; its future after time zero is the same from every atom state. Starting in , the next coordinate law is and subsequent coordinate transitions are , so The hypothesis implies for all after increasing a finite .
Let and for , with . Decompose a visit at time by the first return time and use [F4] and the common atom transition to obtain . The possible event contributes zero. For , absolute convergence and [F8] give Here , so the denominator is legitimate. Since decays geometrically, [F9] gives with holomorphic on . For real , ; hence and [F7] gives .
Let and start the split chain from . By [F6], have mean and . Expanding the square of and summing the geometric covariance bound from step 2.1 gives expectation at most for some finite . On this square equals , so . In particular the stationary split chain hits the atom almost surely; no unproved Harris or skeleton-recurrence theorem is needed.
Near the expression is holomorphic and agrees with on its overlap with . Indeed the denominator has value at and remains nonzero on a sufficiently small disk; the quotient is complex differentiable there by the difference-quotient formula for the derivative of a reciprocal. By [F10], for and , , where . Letting and using [F7] and derivative continuity from [F11] gives . Choose a radius whose closed disk about lies strictly within this holomorphic neighborhood and a finite bound for on its circle. [F12] then bounds these nonnegative derivative values by .
For bounded nonnegative , decompose by the last atom visit at times . A last visit at time contributes , by stationarity and [F5]. The remaining paths have no atom visit through time , and their contribution lies between zero and . Thus Let using step 3.2, then extend from bounded to all nonnegative measurable using [F7] and [F8]. This proves the stationary occupation identity . At the atom state has law ; subsequent transitions are independent of which atom state was chosen, so this argument does not require to be constant on .
For , the finite binomial expansion for each integer , followed by [F8], yields Thus some has . The return is almost surely finite by step 3.1. This argument establishes an exponential moment from positivity and analytic continuation; it does not assume in advance that the original series converges beyond the unit disk.
Fix and let , allowing infinity. The bounded path functions increase to , so [F5] and [F7] give measurability and the Markov identity for this nonnegative function by truncation. On , . For , on , the residual time to is ; the Markov identity and step 4.2 give The last moment is finite because and step 5.1 bounds . For the coordinate chain initialized with the prescribed flag, its first visit is no later than its first atom visit . Consequently is measurable by the same bounded-path approximation and . This proves the conclusion, including the null-set qualification.
Depends on
- Canonical Markov chain on path space
- Cauchy's inequalities bound the Taylor coefficients by the circle supremum
- The sum of a complex power series is analytic throughout its open disc of convergence
- A complex power-series sum has complex derivatives of every order, obtained by repeated termwise differentiation
- The Axiom of Choice
- Invariant and stationary distribution for a Markov kernel
- Measure kernel and probability kernel
- Small-set minorization and geometric drift
- All higher complex derivatives exist and satisfy Cauchy's integral formula on an interior circle
- Every complex analytic function is holomorphic
- Discrete strong Markov property
- Invariant initial law makes a Markov chain stationary
- Markov property for bounded future path functionals
- Measurability of integration against a kernel
- Monotone convergence for the integral
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
Dependency tree · two levels
65 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
- Meyn and Tweedie, Markov Chains and Stochastic Stability, Chapters 13 and 15, regeneration and local geometric convergence (standard reference, not scraped)