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A sticky general-state chain can converge without a geometric rate
Statement
On , let be Lebesgue probability and Then is Lebesgue-irreducible, aperiodic and Harris recurrent, with invariant law . It converges from every state, but it is not geometrically ergodic in the standard sense: there are no and function , finite -almost everywhere, such that for -almost every .
Facts & Assumptions
Given: The state space, refresh kernel and Lebesgue probability in the Statement.
Phi-irreducibility for a general-state Markov kernel: every positive-Lebesgue-measure set is accessible.
Aperiodicity for a general-state Markov kernel: no positive-mass cyclic decomposition exists.
Harris recurrence relative to an irreducibility measure: every positive-Lebesgue-measure set must be hit almost surely from every state.
Total variation distance for probability laws: event differences lower-bound total variation.
General-state Markov-chain convergence under recurrence conditions: an irreducible aperiodic Harris chain with invariant probability converges from every state.
Measure kernel and probability kernel: each section is a probability and measurable evaluation in the state gives a probability kernel.
Canonical Markov chain on path space: under Choice, every specified probability kernel and initial probability admit a canonical Markov path law.
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].
For any with , , so [F1] holds. For , the kernel has a positive self-loop of mass ; since , a cyclic decomposition with positive -mass classes would contain a -positive set of states with positive self-loop probability, contradicting deterministic movement to a disjoint next class. Thus the chain is aperiodic.
To realize refreshes, use the marked state space and the probability kernel , . 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 . Apply [F8] with initial state . Direct multiplication of the transition probabilities gives probability that the first marked refresh occurs at time with new state in . Summing over shows that the waiting time is finite almost surely and the new state has law . The same finite-history calculation, iterated for any finite number of marked refreshes, gives independent refresh states and almost-surely finite waiting times. A countable intersection then gives infinitely many refreshes almost surely. For , the probability that the first refresh draws all miss is . Hence every positive- set is hit almost surely from every start, proving [F3].
The one-step law under has a holding part with density and a refresh part of total mass , distributed as . For every measurable , Thus is invariant. By [F6], convergence holds from every state.
Fix any and set . Since , this set meets every full--measure set on which a proposed geometric bound with rate holds and its multiplier is finite. For each , a refresh draw has no atom at , so , while . Hence . Choose in the intersection just described. Then , so no finite can satisfy for every . As this holds for every , the chain is not geometrically ergodic, despite convergence from every start.
Depends on
- Canonical Markov chain on path space
- The Axiom of Choice
- Aperiodicity for a general-state Markov kernel
- Harris recurrence relative to an irreducibility measure
- Invariant and stationary distribution for a Markov kernel
- Measure kernel and probability kernel
- Phi-irreducibility for a general-state Markov kernel
- Total variation distance for probability laws
- General-state Markov-chain convergence under recurrence conditions
Used by
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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)