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A geometrically ergodic chain has a π\piπ-integrable rate constant

Disproved
MarkovChainCLT.geometricallyErgodic_integrable_rate

by LukeBernese · Aug 15, 2026 · Mathlib 0df444a (Lean v4.33.1)

ergodicitymarkov-chainsprobability

⚠️ Retired — specification defect

The Lean statement below does not encode the problem shown on this page, so its Disproved status carries no information about that problem. Do not import this node or use it as a dependency.

Let XXX be a Harris ergodic Markov chain with invariant distribution π\piπ which is geometrically ergodic, i.e. there are a function M≥0M \ge 0M≥0 and a constant t<1t < 1t<1 with

∥Pn(x,⋅)−π∥≤M(x) tn(n≥1).\|P^n(x, \cdot) - \pi\| \le M(x)\, t^n \qquad (n \ge 1).∥Pn(x,⋅)−π∥≤M(x)tn(n≥1).

Then the rate constant may be taken π\piπ-integrable: there exist M′≥0M' \ge 0M′≥0 with EπM′<∞E_\pi M' < \inftyEπ​M′<∞ and t′<1t' < 1t′<1 such that ∥Pn(x,⋅)−π∥≤M′(x) (t′)n\|P^n(x,\cdot) - \pi\| \le M'(x)\,(t')^n∥Pn(x,⋅)−π∥≤M′(x)(t′)n for all xxx and all n≥1n \ge 1n≥1.

This is the standard drift-function form of geometric ergodicity: a Harris ergodic chain is geometrically ergodic if and only if it satisfies a geometric drift condition ΔV≤−dV+b IC\Delta V \le -dV + b\,I_CΔV≤−dV+bIC​ for a small set CCC and some V≥1V \ge 1V≥1, and the resulting rate constant may be taken proportional to VVV, which satisfies EπV<∞E_\pi V < \inftyEπ​V<∞ (Meyn & Tweedie 1993, Theorems 15.0.1 and 16.0.1; Roberts & Rosenthal 1997).

The integrability EπM<∞E_\pi M < \inftyEπ​M<∞ is exactly the standing side condition under which the source's Theorem 2(ii) converts a total-variation rate into a bound on the strong mixing coefficients, α(n)≤γ(n)EπM\alpha(n) \le \gamma(n) E_\pi Mα(n)≤γ(n)Eπ​M. It is therefore what allows the geometric cases of Corollary 2 and Corollary 3 — which assume only geometric ergodicity, with no explicit moment condition on the rate constant — to be routed through the mixing machinery.

Why this node was retired

The posted statement is

theorem MarkovChainCLT.geometricallyErgodic_integrable_rate {X : Type*} [MeasurableSpace X]
    (P : Kernel X X) [IsMarkovKernel P] (π : Measure X) [IsProbabilityMeasure π]
    (hP : HarrisErgodic P π) (hgeo : GeometricallyErgodic P π) :
    ∃ M : X → ℝ, ∃ t : ℝ, (∀ x, 0 ≤ M x) ∧ Integrable M π ∧ 0 ≤ t ∧ t < 1 ∧
      ErgodicWithRate P π M (fun n => t ^ n) := by sorry

An arbitrary non-countably-generated measurable space invalidates the integrable geometric-rate conclusion.

The recorded counterexample refutes the statement as encoded. It says nothing about the problem shown above, which is a different proposition.

Proposed corrected statement

State the ergodic theorem on a standard Borel/countably generated state space, with the actual Harris irreducibility/aperiodicity hypotheses of the source; retain geometric ergodicity and invariant probability law. Verify the resulting integrable rate theorem separately.

Diagnosis and correction from the public Prove2Me statement audit (wamlat/prove2me-errors). The correction is natural-language mathematics and is not Lean-verified — it is a specification for a corrected node, not a drop-in replacement. No corrected replacement node exists yet.

Preamble
import Definitions.Def_MarkovErgodicity
import Definitions.Def_MarkovChainPathMeasure
import Definitions.Def_MixingCoefficients

open MeasureTheory ProbabilityTheory Filter
open scoped ENNReal NNReal Topology ProbabilityTheory
Formal statement
theorem MarkovChainCLT.geometricallyErgodic_integrable_rate {X : Type*} [MeasurableSpace X]
    (P : Kernel X X) [IsMarkovKernel P] (π : Measure X) [IsProbabilityMeasure π]
    (hP : HarrisErgodic P π) (hgeo : GeometricallyErgodic P π) :
    ∃ M : X → ℝ, ∃ t : ℝ, (∀ x, 0 ≤ M x) ∧ Integrable M π ∧ 0 ≤ t ∧ t < 1 ∧
      ErgodicWithRate P π M (fun n => t ^ n) := by sorry
Source
G. L. Jones, "On the Markov Chain Central Limit Theorem", Probability Surveys 1 (2004) 299-320, arXiv math/0409112v2, Section 2 (eq. (3) and the geometric ergodicity discussion, arXiv v2 pp. 3-4) together with Section 3 (Theorem 2(2)); the underlying drift characterisation is Meyn & Tweedie (1993), Theorems 15.0.1 and 16.0.1.

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