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A sticky general-state chain can converge without a geometric rate

Statement

On X=(0,1], let λ be Lebesgue probability and P(x,A)=(1−x)1A(x)+xλ(A). Then P is Lebesgue-irreducible, aperiodic and Harris recurrent, with invariant law π(dx)=dx/(2x). It converges from every state, but it is not geometrically ergodic in the standard sense: there are no r∈(0,1) and function M:X→[0,+∞], finite π-almost everywhere, such that ∥Pn(x,⋅)−π∥TV≤M(x)rn(n≥1) for π-almost every x.

Facts & Assumptions

Given: The state space, refresh kernel and Lebesgue probability in the Statement.

[F1]

Phi-irreducibility for a general-state Markov kernel: every positive-Lebesgue-measure set is accessible.

[F2]

Aperiodicity for a general-state Markov kernel: no positive-mass cyclic decomposition exists.

[F3]

Harris recurrence relative to an irreducibility measure: every positive-Lebesgue-measure set must be hit almost surely from every state.

[F5]

Total variation distance for probability laws: event differences lower-bound total variation.

[F6]

General-state Markov-chain convergence under recurrence conditions: an irreducible aperiodic Harris chain with invariant probability converges from every state.

[F7]

Measure kernel and probability kernel: each section is a probability and measurable evaluation in the state gives a probability kernel.

[F8]

Canonical Markov chain on path space: under Choice, every specified probability kernel and initial probability admit a canonical Markov path law.

[A1]

Assume The Axiom of Choice for [F8] and the inherited path-law assumptions of [F6].

Verification

Given: The kernel and facts [F1]–[F7], with [A1].

1.1F1F2F7given

For any A with λ(A)>0, P(x,A)≥xλ(A)>0, so [F1] holds. For x<1, the kernel has a positive self-loop of mass 1−x; since π({1})=0, a cyclic decomposition with positive π-mass classes would contain a π-positive set of states with positive self-loop probability, contradicting deterministic movement to a disjoint next class. Thus the chain is aperiodic.

1.2F3F7F8A1givenconstructalgebra

To realize refreshes, use the marked state space X×{0,1} and the probability kernel K((x,b),A×{1})=xλ(A), K((x,b),A×{0})=(1−x)1A(x). Every measurable set is a disjoint union of these two slice forms, so [F7] verifies countable additivity and measurable evaluations. Its first-coordinate transition is P. Apply [F8] with initial state (x,0). Direct multiplication of the transition probabilities gives probability (1−x)t−1xλ(A) that the first marked refresh occurs at time t≥1 with new state in A. Summing over t shows that the waiting time is finite almost surely and the new state has law λ. The same finite-history calculation, iterated for any finite number of marked refreshes, gives independent λ refresh states and almost-surely finite waiting times. A countable intersection then gives infinitely many refreshes almost surely. For λ(A)>0, the probability that the first N refresh draws all miss A is (1−λ(A))N→0. Hence every positive-λ set is hit almost surely from every start, proving [F3].

2.1F4F6step 1.2

The one-step law under π has a holding part with density (1−x)/(2x) and a refresh part of total mass ∫xπ(dx)=1/2, distributed as λ. For every measurable A, ∫P(x,A)π(dx)=∫A1−x2xdx+12λ(A)=∫Adx2x=π(A). Thus π is invariant. By [F6], convergence holds from every state.

3.1F5givenstep 2.1algebra∎

Fix any r∈(0,1) and set Br=(0,(1−r)2). Since π(Br)=1−r>0, this set meets every full-π-measure set on which a proposed geometric bound with rate r holds and its multiplier is finite. For each x∈Br, a refresh draw has no atom at x, so Pn(x,{x})=(1−x)n, while π({x})=0. Hence ∥Pn(x,⋅)−π∥TV≥(1−x)n. Choose x in the intersection just described. Then 1−x>r, so no finite M(x) can satisfy (1−x)n≤M(x)rn for every n≥1. As this holds for every r∈(0,1), the chain is not geometrically ergodic, despite convergence from every start.

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