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A geometrically ergodic chain has a small set of positive invariant measure

Proved
MarkovChainCLT.exists_isSmallSet_measure_pos

by Nickrobbins95 · Sep 6, 2026 · Mathlib c5ea003 (Lean v4.30.0)

ergodicitymarkov-chainsprobability

Let PPP be a Markov transition kernel on a state space X\mathsf{X}X whose σ\sigmaσ-field is countably generated, let π\piπ be an invariant probability measure, and suppose the chain is Harris ergodic and geometrically ergodic: there are a function M≥0M \ge 0M≥0 and a constant t<1t < 1t<1 with ∥Pn(x,⋅)−π∥≤M(x) tn\|P^n(x,\cdot) - \pi\| \le M(x)\,t^n∥Pn(x,⋅)−π∥≤M(x)tn for every xxx and every n≥1n \ge 1n≥1. Then the chain possesses a measurable small set of positive invariant measure: there is a measurable C⊆XC \subseteq \mathsf{X}C⊆X with π(C)>0\pi(C) > 0π(C)>0 for which one can find an integer n0≥1n_0 \ge 1n0​≥1, a constant ε>0\varepsilon > 0ε>0 and a probability measure QQQ satisfying the minorization Pn0(x,A)≥ε Q(A)P^{n_0}(x, A) \ge \varepsilon\, Q(A)Pn0​(x,A)≥εQ(A) simultaneously for every x∈Cx \in Cx∈C and every measurable AAA.

This is the measure-theoretic half of Meyn and Tweedie's Theorem 15.0.1 (i) ⇒\Rightarrow⇒ (iii), isolated from its analytic half. In the classical treatment the small set is produced by Theorem 5.2.2 from ψ\psiψ-irreducibility, by Nummelin splitting together with a rectangle-extraction argument. Under geometric ergodicity that machinery is unnecessary, and the proof given here avoids it. Write fn(x,y)f_n(x, y)fn​(x,y) for the Radon-Nikodym density of the absolutely continuous part of Pn(x,⋅)P^n(x,\cdot)Pn(x,⋅) with respect to π\piπ, which is jointly measurable precisely because the σ\sigmaσ-field is countably generated. The elementary observation driving the argument is that if π(A)≤μ(A)+u\pi(A) \le \mu(A) + uπ(A)≤μ(A)+u for every measurable AAA, then π{y:(dμ/dπ)(y)<1/2}≤2u\pi\{y : (\mathrm{d}\mu/\mathrm{d}\pi)(y) < 1/2\} \le 2uπ{y:(dμ/dπ)(y)<1/2}≤2u; applied with μ=Pn(x,⋅)\mu = P^n(x,\cdot)μ=Pn(x,⋅) and u=M(x)tnu = M(x)t^nu=M(x)tn, this says that the set of states where the nnn-step density is small is itself small in measure, not merely of positive complement, and it is this quantitative strengthening that removes the need for rectangle extraction. Cutting the state space along a level set of x↦π{y:fn(x,y)<1/2}x \mapsto \pi\{y : f_n(x,y) < 1/2\}x↦π{y:fn​(x,y)<1/2} produces a set CCC of positive π\piπ-measure on which that bound is uniform; one application of Tonelli's theorem on π×π\pi \times \piπ×π, followed by Markov's inequality, yields a set DDD with π(D)≥1/2\pi(D) \ge 1/2π(D)≥1/2 whose points are reached with density at least 1/21/21/2 from all but a quarter of CCC. Composing the two half-steps gives P2n(x,A)≥18 π(C) π(D∩A)P^{2n}(x, A) \ge \tfrac{1}{8}\,\pi(C)\,\pi(D \cap A)P2n(x,A)≥81​π(C)π(D∩A) for every x∈Cx \in Cx∈C, which is the required minorization with n0=2nn_0 = 2nn0​=2n, ε=18π(C)π(D)\varepsilon = \tfrac{1}{8}\pi(C)\pi(D)ε=81​π(C)π(D) and QQQ the normalized restriction of π\piπ to DDD.

Two remarks on the scope of the statement. First, the conclusion is not free. The identity kernel on R\mathbb{R}R leaves the uniform law on [0,1][0,1][0,1] invariant, yet under it every small set is a singleton or empty and therefore null, so no measurable small set of positive measure exists: some ergodicity hypothesis is genuinely doing work. Second, countable generation of the σ\sigmaσ-field is load-bearing rather than decorative, and enters twice — as the hypothesis of the kernel Radon-Nikodym theorem that makes the densities fnf_nfn​ jointly measurable, and as the assumption that excludes the countable/co-countable state spaces on which the accepted refutation of the sibling problem MarkovChainCLT.geometricallyErgodic_integrable_rate is built, and on which the present statement is in fact false. It should be recorded that the proof consumes only the geometric total-variation bound: the invariance of π\piπ carried by HarrisErgodic is never used, and the hypothesis is retained solely so that the statement matches the binder list of the parent problem MarkovChainCLT.geoDriftCondition_of_geometricallyErgodic.

Preamble
import Definitions.Def_MarkovErgodicity
import Definitions.Def_MarkovDriftMinorization

open MeasureTheory ProbabilityTheory Filter
open scoped ENNReal NNReal Topology ProbabilityTheory
Formal statement
theorem MarkovChainCLT.exists_isSmallSet_measure_pos {X : Type*} [MeasurableSpace X]
    [MeasurableSpace.CountablyGenerated X]
    (P : Kernel X X) [IsMarkovKernel P] (π : Measure X) [IsProbabilityMeasure π]
    (hP : HarrisErgodic P π) (hgeo : GeometricallyErgodic P π) :
    ∃ C : Set X, MeasurableSet C ∧ IsSmallSet P C ∧ 0 < π C := by sorry
Source
G. L. Jones, "On the Markov Chain Central Limit Theorem", Probability Surveys 1 (2004) 299-320, arXiv math/0409112v2, Section 2 (eqs. (3) and (4)). Original: S. P. Meyn & R. L. Tweedie, Markov Chains and Stochastic Stability, Springer 1993 (2nd ed., Cambridge University Press 2009): Theorem 5.2.2 (existence of small sets), under the standing assumption of Section 3.1 (countably generated sigma-field). This is the measure-theoretic half of the implication (i) => (iii) of their Theorem 15.0.1, specialized to the geometrically ergodic case: where Theorem 5.2.2 extracts a small set from psi-irreducibility via Nummelin splitting, the statement here obtains one directly from the geometric total-variation rate, and the invariant measure supplies psi, so the resulting small set has positive invariant measure. It is the hypothesis 'IsSmallSet P C' consumed by MarkovChainCLT.ergodicWithRate_of_geoDriftCondition, MarkovChainCLT.integrable_of_geoDriftCondition, MarkovChainCLT.clt_of_geometric_drift and MarkovChainCLT.clt_of_polynomial_drift, and the set demanded by the conclusion of MarkovChainCLT.geoDriftCondition_of_geometricallyErgodic.

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