IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v22y2016i2p109-116n2.html
   My bibliography  Save this article

On the complexity of binary floating point pseudorandom generation

Author

Listed:
  • Nekrutkin Vladimir

    (St.Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034, Russia)

Abstract

The paper is devoted to the complexity analysis of binary floating point pseudorandom generators. We start with a stochastic model of a “usual” pseudorandom generator (PRNG). Then integer outputs of this generator are transformed into i.i.d. random variables, agreed with an abstract binary floating point system. Additionally, these random variables are approximately uniformly distributed on the interval [0,1]. Therefore, they can interpreted as (random) outputs of a binary floating point pseudorandom generator (flPRNG). The simulation complexity of such a transformation is defined as the average number of PRNG's outputs necessary to produce the unique output of flPRNG. Several transformations with minimal or approximately minimal complexities are presented and discussed.

Suggested Citation

  • Nekrutkin Vladimir, 2016. "On the complexity of binary floating point pseudorandom generation," Monte Carlo Methods and Applications, De Gruyter, vol. 22(2), pages 109-116, June.
  • Handle: RePEc:bpj:mcmeap:v:22:y:2016:i:2:p:109-116:n:2
    DOI: 10.1515/mcma-2016-0105
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2016-0105
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/mcma-2016-0105?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Romik, Dan, 1999. "Sharp entropy bounds for discrete statistical simulation," Statistics & Probability Letters, Elsevier, vol. 42(3), pages 219-227, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:mcmeap:v:22:y:2016:i:2:p:109-116:n:2. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.