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.
Bibliographical noteNOTICE: 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
- Bayesian inference
- communication theory