Dynamical systems and ergodic theory Definition
Unique ergodicity
Builds on Metric spaceCompact spaceBorel measureProbability measureInvariant measureKrylov–Bogolyubov theorem
Definition
Let be a compact metric space and let be a continuous map. A Borel probability measure on is -invariant if for every Borel set . The map is uniquely ergodic if there is exactly one -invariant Borel probability measure on .
Remarks
By the Krylov–Bogolyubov theorem the set of invariant Borel probability measures is never empty, so unique ergodicity asks that it has a single element.
Example: let , let be irrational and let . If is invariant, its Fourier coefficients satisfy . For the factor is not , so . A measure on the circle is determined by its Fourier coefficients, so is Lebesgue measure, and is uniquely ergodic.
Non-example: the doubling map on is continuous and fixes . Both the point mass at and Lebesgue measure are invariant, so is not uniquely ergodic.
Equivalent form: is uniquely ergodic if and only if, for every continuous , the averages converge uniformly in to a constant. The constant is then for the unique invariant measure .
Reading path
To understand this page from the ground up, read these 14 pages in order. The foundations come first.
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- no evidence level
- Borel measure concept
- Compact set concept
- Compact space concept
- Complete metric space concept
- Continuous function concept
- Invariant measure concept
- Metric space concept
- Probability measure concept
- Separable space concept
- Tightness of measures concept
- Weak convergence of measures concept
- Prokhorov's theorem statement
- Krylov–Bogolyubov theorem statement
- Unique ergodicity definition
Further reading
About this page
| Tag | KH07KHXJB6 |
|---|---|
| URI | m2://KH07KHXJB6 |
| Kind | definition |
| Writing | W0 stub |
| MSC | 37A25, 37B05 |
| Defines | Unique ergodicity |
| Uses | Metric space, Compact space, Borel measure, Probability measure, Invariant measure, Krylov–Bogolyubov theorem |
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