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CounterexampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passaudited 2026-10-02
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A stationary chain need not be ergodic

Statement refuted

A strictly stationary Markov chain need not be ergodic. On E={0,1} with the identity transition matrix and π=(1/2,1/2), the chain started from π is strictly stationary, but the strictly shift-invariant path event "zero occurs infinitely often" has probability 1/2; the canonical shift therefore fails to be ergodic (Ergodicity relative to an invariant measure). The example is also reducible, so it does not contradict the ergodicity theorem for irreducible positive-recurrent chains.

Facts & Assumptions

Given: The two-point state space E={0,1}, the identity transition matrix P, the probability π=(1/2,1/2), and the P-chain X started from π on the canonical path space EN0.

[F1]

A transition matrix has nonnegative entries with every row summing to one. (Transition matrices and n-step probabilities)

[F2]

A probability vector π is invariant for a countable transition matrix exactly when π(y)=∑xπ(x)p(x,y) for every y. (Invariant and stationary distribution for a Markov kernel)

[F3]

A process is strictly stationary when its finite-dimensional laws are unchanged by nonnegative time shifts; its canonical path law is the pushforward under the coordinate map, the left shift is θ(z)n=zn+1, and a strictly stationary process is ergodic when θ is ergodic for that path law. (Stationary process and canonical path shift)

[F4]

A measure-preserving system is ergodic for μ exactly when every strictly invariant event A (that is, T−1A=A) has μ(A)=0 or μ(X∖A)=0. (Ergodicity relative to an invariant measure)

Counterexample

Given: E={0,1}, the identity matrix P, the law π=(1/2,1/2), and the chain X started from π.

Proof technique: identify the canonical path law explicitly, exhibit a strictly shift-invariant event of intermediate probability, and conclude non-ergodicity.

1.1F1F2given

The identity matrix P=(1001) is a transition matrix, and π is invariant: (πP)(0)=π(0)⋅1=1/2=π(0) and likewise at 1, which is exactly the identity of [F2].

1.2F3given

The event A:={z∈EN0:zn=0 for infinitely many n}=⋂m≥0⋃n≥m{z:zn=0} is a countable Boolean combination of coordinate events and is therefore measurable.

2.1F3step 1.1given

Both states are absorbing, so Xn=X0 for every n≥0; hence every finite-dimensional law of X is the law of the constant tuple (X0,…,X0), which is unchanged by any nonnegative time shift, and the chain is strictly stationary in the sense of [F3]. Its canonical path law is Pπ=12δ0ˉ+12δ1ˉ, where 0ˉ=(0,0,0,…) and 1ˉ=(1,1,1,…).

2.2F3step 1.2given

The event A is strictly shift-invariant: θ−1A={z:θz∈A}={z:zn+1=0 for infinitely many n}=A, because deleting the first coordinate of a sequence does not change whether infinitely many of its entries vanish.

3.1F3step 2.1given

The left shift preserves Pπ: by step 2.1 the path law is supported on the two fixed paths 0ˉ,1ˉ, and θ0ˉ=0ˉ, θ1ˉ=1ˉ, so Pπ(θ−1B)=Pπ(B) for every measurable B.

4.1F4step 2.2step 3.1given

Evaluating at A: 0ˉ∈A and 1ˉ∉A, so Pπ(A)=12, and by step 2.2 this is the measure of a strictly invariant event; since 12∉{0,1}, [F4] shows that the canonical shift is not ergodic, even though Pπ is shift-invariant by step 3.1.

5.1F2F3F4step 2.1step 4.1given∎

Boundary and axiom cases: the event A is strictly invariant, not merely invariant modulo null sets, and step 2.2 verifies the identity on the whole path space; the value 1/2 is neither 0 nor 1, so the criterion of [F4] genuinely fails; Ac and the events "infinitely many ones" behave the same way; if π were concentrated on 0 or on 1 the chain would be ergodic, so the mixture is essential; the chain is reducible with two communicating classes {0} and {1}, which is exactly why the ergodicity result for irreducible chains does not apply; the explicit description of Pπ in step 2.1 makes no selection and no choice principle is used; and no convergence claim is made, the example refuting only the implication "stationary ⇒ ergodic".

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