A Markov chain is irreducible when every state can reach every other state along a path of positive-probability transitions.
A Markov chain with transition kernel P is φ-irreducible if φ is a nonzero sigma-finite measure and every measurable set A with φ(A)>0 satisfies ∑n≥1Pn(x,A)>0 from every starting state x. This is positive reachability, not certain eventual hitting.
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- Absolute relaxation time
- Bounded conditional moments imply a geometric drift
- Countable-state Markov chain convergence theorem
- Eigenvalue lower bound for total variation mixing
- Ergodic theorem for a finite continuous-time Markov chain
- Gibbs sampling for a finite hidden spin field
- Irreducibility and recurrence pass between a CTMC and its jump chain
- Markov chain invariant measure
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