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m-step Dobrushin certificate for a stationary probability
Statement
Let be a finite column-stochastic matrix and let . Put , where is the zero-sum coefficient from Column-overlap formula for the zero-sum ℓ1 coefficient. If , then P has a unique stationary probability vector . Every column probability vector satisfies
The condition is a certified uniqueness and error condition; when a computed upper bound for is at least one, this theorem supplies no accuracy certificate.
Facts & Assumptions
Given: A finite column-stochastic matrix P, an integer , and .
Finite column-stochastic matrices and probability vectors: acts on column probability vectors, and the zero-sum space is with .
Column-stochastic matrices preserve mass and contract ℓ1: P preserves coordinate sums and satisfies for every real vector z.
Invariant and stationary distribution for a Markov kernel: for the row kernel , a stationary law is an invariant probability row vector ; transposing gives .
Every transition matrix on a nonempty finite state space has a stationary distribution: every finite transition matrix on a nonempty finite state space has an invariant probability; the theorem gives existence only.
Proof
Given: P, m, and q as above, and a column probability vector x.
Proof technique: use contraction on zero-sum errors and telescope the one-step residual.
The transpose is a finite row transition matrix by [F1]. By [F5] it has an invariant probability row vector, and [F4] transposes this to a stationary column probability for P.
If are stationary column probabilities, then lies in H and . By [F3], ; since , this forces . Thus is unique.
Let . Both vectors have coordinate sum one, so . Since , reverse triangle inequality and [F3] give . Dividing by proves the first bound.
The telescoping identity follows by cancellation of adjacent terms. Applying [F2] repeatedly gives for each k; the triangle inequality therefore yields .
Combine steps 2.2 and 3.1. The proof does not require irreducibility or aperiodicity once the stated m-step contraction is certified; if , the division step is unavailable and no bound is asserted.
Depends on
- Finite column-stochastic matrices and probability vectors
- Column-stochastic matrices preserve mass and contract ℓ1
- Column-overlap formula for the zero-sum ℓ1 coefficient
- Invariant and stationary distribution for a Markov kernel
- Every transition matrix on a nonempty finite state space has a stationary distribution
Used by
Dependency tree · two levels
11 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ilse C. F. Ipsen and Teresa M. Selee, Ergodicity Coefficients Defined by Vector Norms (standard reference, not scraped)
- Yuanyuan Liu, Perturbation Bounds for the Stationary Distributions of Markov Chains (standard reference, not scraped)