2004
DOI: 10.1007/bf02789313
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The structure of strongly stationary systems

Abstract: Abstract. Motivated by a problem in ergodic Ramsey theory, Furstenberg and Katznelson introduced the notion of strong stationarity, showing that certain recurrence properties hold for arbitrary measure preserving systems if they are valid for strongly stationary ones. We construct some new examples and prove a structure theorem for strongly stationary systems. The building blocks are Bernoulli systems and rotations on nilmanifolds.

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Cited by 23 publications
(29 citation statements)
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“…It can then be shown that the resulting system ( X, µ, T ) has additional structure, namely it is strongly stationary (see Definition 5.1). The structure of strongly stationary systems was completely determined in [43] and [22], where it was shown that they satisfy properties (i) and (ii) of Theorem 1.6. Unfortunately, we do not know how to establish total ergodicity of the ergodic components of Furstenberg systems of the Liouville function (for the Möbius function this property is not even true).…”
Section: Introduction and Main Resultsmentioning
confidence: 99%
“…It can then be shown that the resulting system ( X, µ, T ) has additional structure, namely it is strongly stationary (see Definition 5.1). The structure of strongly stationary systems was completely determined in [43] and [22], where it was shown that they satisfy properties (i) and (ii) of Theorem 1.6. Unfortunately, we do not know how to establish total ergodicity of the ergodic components of Furstenberg systems of the Liouville function (for the Möbius function this property is not even true).…”
Section: Introduction and Main Resultsmentioning
confidence: 99%
“…In the following we will use I to denote the subgroup of T generated by a subset I ⊂ T. Subsets of R are tacitly identified with their projections mod 1 onto T. Theorem 2.4 (cf. [8,Theorem 6.4]). Let q, r ∈ N, and let (X, B, µ, T ) be an ergodic measure preserving system whose discrete spectrum…”
Section: Remark 22mentioning
confidence: 99%
“…For convergence, the proof also carries over, using the general result on convergence of linear averages in [15]. Furthermore, the generalization of Theorem 5 holds, using the description of the characteristic factors in [15] and the higher analog of identity (11) in [6].…”
Section: Further Generalizationsmentioning
confidence: 98%