IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v365y2006i1p203-210.html
   My bibliography  Save this article

Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

Author

Listed:
  • Neirotti, Juan P.
  • Saad, David

Abstract

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in code division multiple access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also studied.

Suggested Citation

  • Neirotti, Juan P. & Saad, David, 2006. "Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(1), pages 203-210.
    • Handle: RePEc:eee:phsmap:v:365:y:2006:i:1:p:203-210
    DOI: 10.1016/j.physa.2006.01.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437106000616
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2006.01.020?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gzyl, Henryk & ter Horst, Enrique & Molina, German, 2015. "Application of the method of maximum entropy in the mean to classification problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 101-108.

    More about this item

    Keywords

    Communication theory; Bayesian inference;

    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:eee:phsmap:v:365:y:2006:i:1:p:203-210. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.