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A systematic composition of reversible kernels need not be reversible
Statement refuted
The chronological composition of two probability kernels that are each reversible with respect to the same invariant probability must itself be reversible with respect to that probability.
Facts & Assumptions
Given: with its full sigma-algebra, , the transposition swapping and , and the transposition swapping and ; each transposition fixes the remaining state.
, , , and are as specified above.
Reversibility means that the joint flow measure is symmetric under coordinate swap. (General-state reversibility by symmetric flow measure)
Reversibility of a probability kernel with respect to a probability implies invariance of that probability. (General-state reversibility implies invariance)
Kernel composition is in chronological order: first applies and then . (Composition of probability kernels)
The composition of two probability kernels is again a probability kernel. (Kernel composition is well defined and associative)
Invariance means for each measurable . (Invariant and stationary distribution for a Markov kernel)
A probability measure has total mass one. (Probability measures and probability spaces)
A probability kernel has probability-measure rows and measurable event evaluations. (Measure kernel and probability kernel)
Counterexample
Given: The data in [A1]. Define for and .
Proof technique: Check the symmetric flow of each involution, compute the chronological composition, and compare one off-diagonal flow atom with its transpose.
The uniform values sum to one, so [F6] makes a probability; for every set , exactly when and is otherwise zero, so each row is a probability measure and its evaluations are measurable on the finite space by [F7]. The flow has mass at ; since is the identity, its support and masses are symmetric, so both kernels are reversible by [F1].
Chronological composition [F3] gives with updates , , and ; [F4] makes it a probability kernel. Each state has exactly one predecessor, so for every j and [F5] gives .
By [F2] and step 1.1, each component kernel preserves the same uniform probability .
The composite flow has but , so it is not symmetric by [F1]. Thus the composition is invariant but not reversible, although both updates are reversible and invariant by steps 1.1 and 2.1.
Depends on
- Probability measures and probability spaces
- Measure kernel and probability kernel
- Composition of probability kernels
- Kernel composition is well defined and associative
- General-state reversibility by symmetric flow measure
- Invariant and stationary distribution for a Markov kernel
- General-state reversibility implies invariance
Used by
Nothing in the library uses this result yet.
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