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

Juan P. Neirotti; David Saad

doi:10.1016/j.physa.2006.01.020

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

Juan P. Neirotti^*, David Saad

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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. © 2006 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	203-210
Number of pages	8
Journal	Physica A
Volume	365
Issue number	1
DOIs	https://doi.org/10.1016/j.physa.2006.01.020
Publication status	Published - 1 Jun 2006

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Physica A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neirotti, Juan P. and Saad, David (2006). Efficient Bayesian inference for learning in the ising linear perceptron and signal detection in CDMA. Physica A, 365 (1), pp. 203-210. DOI 10.1016/j.physa.2006.01.020

Keywords

Bayesian inference
communication theory

Access to Document

10.1016/j.physa.2006.01.020

Bayesian inference for learning in the Ising linear perceptron
NOTICE: this is the author’s version of a work that was accepted for publication in Physica A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neirotti, Juan P. and Saad, David (2006). Efficient Bayesian inference for learning in the ising linear perceptron and signal detection in CDMA. Physica A, 365 (1), pp. 203-210. DOI 10.1016/j.physa.2006.01.020

Cite this

@article{ab9e82b788754ccc8b92f8bc0c25c33d,

title = "Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA",

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. {\textcopyright} 2006 Elsevier B.V. All rights reserved.",

keywords = "Bayesian inference, communication theory",

author = "Neirotti, {Juan P.} and David Saad",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Physica A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neirotti, Juan P. and Saad, David (2006). Efficient Bayesian inference for learning in the ising linear perceptron and signal detection in CDMA. Physica A, 365 (1), pp. 203-210. DOI 10.1016/j.physa.2006.01.020",

year = "2006",

month = jun,

day = "1",

doi = "10.1016/j.physa.2006.01.020",

language = "English",

volume = "365",

pages = "203--210",

journal = "Physica A",

issn = "0378-4371",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

AU - Neirotti, Juan P.

AU - Saad, David

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Physica A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neirotti, Juan P. and Saad, David (2006). Efficient Bayesian inference for learning in the ising linear perceptron and signal detection in CDMA. Physica A, 365 (1), pp. 203-210. DOI 10.1016/j.physa.2006.01.020

PY - 2006/6/1

Y1 - 2006/6/1

N2 - 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. © 2006 Elsevier B.V. All rights reserved.

AB - 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. © 2006 Elsevier B.V. All rights reserved.

KW - Bayesian inference

KW - communication theory

UR - http://www.scopus.com/inward/record.url?scp=33645980320&partnerID=8YFLogxK

U2 - 10.1016/j.physa.2006.01.020

DO - 10.1016/j.physa.2006.01.020

M3 - Article

SN - 0378-4371

VL - 365

SP - 203

EP - 210

JO - Physica A

JF - Physica A

IS - 1

ER -

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

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this