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A phi-irreducible chain can fail to converge from a pi-null start
Statement
On define and, for , Then is -irreducible and aperiodic for , has invariant law , and is not Harris recurrent. From every , fails to converge to in total variation.
Facts & Assumptions
Given: The state space and transition rule in the Statement.
Phi-irreducibility for a general-state Markov kernel: every set of positive -measure is reachable from every state with positive probability at some finite time.
Aperiodicity for a general-state Markov kernel: a cyclic decomposition needs at least two positive- classes.
Harris recurrence relative to an irreducibility measure: Harris recurrence requires almost-sure hitting of every positive- set from every start.
Invariant and stationary distribution for a Markov kernel: an invariant probability is unchanged by one transition.
Total variation distance for probability laws: total variation is the supremum over measurable events.
Measure kernel and probability kernel: a transition kernel has probability sections and measurable evaluations.
Verification
Given: The transition rule and facts [F1]–[F5].
The only nonempty -positive set contains state . From it is already reached, and from the one-step probability of reaching it is . Thus the chain is -irreducible. The point mass is invariant because state is absorbing. Since has only one positive-mass state, no partition into classes all of positive -mass exists; the chain is aperiodic under [F2].
Starting from , the probability of avoiding absorption through time is The identity follows by writing and canceling adjacent factors. Thus , so the probability of ever hitting the -positive set is less than one. The chain is not Harris recurrent by [F3].
At time , the mass outside is exactly . By [F5], which has positive limit. Therefore the stationary-a.e. convergence conclusion cannot be strengthened to all starts without Harris recurrence.
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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)