• DocumentCode
    985429
  • Title

    Generalized Box–MÜller Method for Generating q -Gaussian Random Deviates

  • Author

    Thistleton, W.J. ; Marsh, J.A. ; Nelson, Karl ; Tsallis, C.

  • Author_Institution
    Dept. of Math., SUNY Inst. of Technol., Utica, NY
  • Volume
    53
  • Issue
    12
  • fYear
    2007
  • Firstpage
    4805
  • Lastpage
    4810
  • Abstract
    The q-Gaussian distribution is known to be an attractor of certain correlated systems and is the distribution which, under appropriate constraints, maximizes a generalization of the familiar Shannon entropy. This generalized entropy, or q-entropy, provides the basis of nonextensive statistical mechanics, a theory which is postulated as a natural extension of the standard (Boltzmann-Gibbs) statistical mechanics, and which may explain the ubiquitous appearance of heavy-tailed distributions in both natural and man-made systems. The q-Gaussian distribution is also used as a numerical tool, for example as a visiting distribution in Generalized Simulated Annealing. A simple, easy to implement numerical method for generating random deviates from a q-Gaussian distribution based upon a generalization of the well known Box-Miiller method is developed and presented. This method is suitable for a larger range of q values, -infin < q < 3, than has previously appeared in the literature, and can generate deviates from q-Gaussian distributions of arbitrary width and center. MATLAB code showing a straightforward implementation is also included.
  • Keywords
    Gaussian distribution; entropy; random number generation; statistical mechanics; correlated systems; generalized Box-Muller method; heavy-tailed distributions; nonextensive statistical mechanics; q-Gaussian distribution; q-Gaussian random deviate generation; q-entropy; Entropy; Gaussian distribution; Iron; MATLAB; Probability; Quantum computing; Quantum mechanics; Random number generation; Signal processing algorithms; Simulated annealing; ${rm q}$-Gaussian distribution; Entropy maximizing distribution; random number generation;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2007.909173
  • Filename
    4385787