Alphabeta Math
PropositionStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-13
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  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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Inducing an ergodic system gives an ergodic system

Statement

If T is an ergodic probability-preserving transformation and μ(E)>0, then TE on E∞ is ergodic for μE.

Facts & Assumptions

[F1]

The induced transformation preserves its probability measure. Induced transformations preserve restricted finite measure.

[F2]

Positive sets in an ergodic probability system have conull forward entrance sets. Positive sets sweep out ergodic probability systems.

[F3]

An everywhere invariant finite real measurable function is a.e. constant, and this property characterizes ergodicity. Equivalent invariant-set and invariant-function criteria for ergodicity.

Proof

Given: If T is an ergodic probability-preserving transformation and μ(E)>0, then TE on E∞ is ergodic for μE.

1.1F1F2

Let U=⋃n≥0T−nE∞. It is measurable and conull by positive-set sweep-out. It is strictly invariant: if Tx eventually enters the core then x does; if x eventually enters at a positive time then Tx does, while if x is already in the core, its next positive return belongs to the core. Thus T−1U=U. On U let q(x)=min⁡{n≥0:Tnx∈E∞}. The fibers {q=n}=T−nE∞∖⋃j<nT−jE∞ are measurable.

2.1step 1.1F1

Take any finite real measurable f on the core with f∘TE=f everywhere. Define F(x)=f(Tq(x)x) for x∈U and F(x)=0 otherwise. The measurable q-fibers make F measurable. If x∈U∖E∞, then q(Tx)=q(x)−1, hence F(Tx)=F(x). If x∈E∞, then q(Tx)=rE(x)−1: before the first return there is no E visit, and the first return is in the core. Therefore F(Tx)=f(TEx)=f(x)=F(x). On X∖U, both x and Tx are outside U and F is zero.

3.1step 2.1F1F3∎

Ergodicity of T and the everywhere invariant-function criterion give a constant c with F=c almost everywhere. Since F=f on the core, f=c for μE-almost every point there. The same criterion applied to the probability-preserving T_E now proves its ergodicity. Working with everywhere invariant functions avoids choosing representatives for almost-everywhere invariance.

Depends on

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Sources