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A uniformly ergodic adaptive refresh family
Example
Let with uniform target , let , , and for set . Use the deterministic adaptation . The adaptive chain satisfies diminishing adaptation and its family is simultaneously uniformly ergodic, so its marginal law converges to .
Facts & Assumptions
Given: The two-state space, uniform probability , kernels , and deterministic sequence in the Example.
Measure kernel and probability kernel: a probability kernel has probability rows and measurable event evaluations.
General-state reversibility by symmetric flow measure: a kernel is reversible when its joint flow measure under is symmetric.
General-state reversibility implies invariance: reversibility implies .
Total variation distance for probability laws: total variation is the supremum over measurable events.
Diminishing adaptation: is the uniform-in-state total variation change between successive kernels.
Adaptive MCMC marginal convergence under diminishing adaptation with uniform ergodicity or containment: diminishing adaptation and simultaneous uniform ergodicity imply marginal convergence.
Verification
Given: [F1]–[F6] and the finite state space and target in the Example.
The matrix of is Its entries are continuous in , so the family is jointly measurable on the stated spaces; its rows are probability vectors by and [F1]. The matrix is symmetric, and assigns equal mass to both states, so the four joint-flow atoms are symmetric under coordinate swap. Thus is reversible by [F2], and is invariant by [F3].
The kernels satisfy , so . This is diminishing adaptation by [F5].
Since and , induction gives For either state , , so uniformly in and . Therefore the family is simultaneously uniformly ergodic.
Steps 2.1 and 1.2 verify the two hypotheses in alternative 1 of [F6]. Hence for every initial state.
Depends on
- Diminishing adaptation
- General-state reversibility by symmetric flow measure
- Invariant and stationary distribution for a Markov kernel
- Measure kernel and probability kernel
- Total variation distance for probability laws
- Adaptive MCMC marginal convergence under diminishing adaptation with uniform ergodicity or containment
- General-state reversibility implies invariance
Used by
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Sources
- Gareth O. Roberts and Jeffrey S. Rosenthal, Coupling and Ergodicity of Adaptive MCMC (standard reference, not scraped)