• Jan NaudtsEmail author


The parameters of a statistical model can themselves be stochastic variables. This leads to the notion of hyperensembles. Superstatistics is a recent development in this direction. But here, the approach is used to derive the canonical ensemble from the microcanonical one.


Probability Distribution Canonical Ensemble Inverse Temperature Point Vortex Gibbs Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abe, S., Beck, C., Cohen, E.G.D.: Superstatistics, thermodynamics, and fluctuations. Phys. Rev. E 76, 031102 (2007) CrossRefGoogle Scholar
  2. 2.
    Beck, C.: Dynamical foundations of nonextensive statistical mechanics. Phys. Rev. Lett. 87, 180601 (2001) CrossRefMathSciNetGoogle Scholar
  3. 3.
    Beck, C., Cohen, E.: Superstatistics. Physica A 322, 267–275 (2003) zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Chavanis, P.H., Sire, C.: Statistics of velocity fluctuations arising from a random distribution of point vortices: The speed of fluctuations and the diffusion coefficient. Phys. Rev. E 62, 490–506 (2000) CrossRefMathSciNetGoogle Scholar
  5. 5.
    Crooks, G.: Beyond Boltzmann-Gibbs statistics: Maximum entropy hyperensembles out-of-equilibrium. Phys. Rev. E 75, 041119 (2007) CrossRefGoogle Scholar
  6. 6.
    Rajagopal, A.K.: Superstatistics - a quantum generalization. arXiv: cond-mat/0608679 (2006)
  7. 7.
    Van der Straeten, E., Beck, C.: Superstatistical distributions from a maximum entropy principle. Phys. Rev. E 78, 051101 (2008) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Physics DepartmentUniversity of AntwerpAntwerpBelgium

Personalised recommendations