English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we introduce a new support recovery algorithm from noisymeasurements called Bayesian hypothesis test via belief propagation (BHT-BP).BHT-BP focuses on sparse support recovery rather than sparse signal estimation.The key idea behind BHT-BP is to detect the support set of a sparse vectorusing hypothesis test where the posterior densities used in the test areobtained by aid of belief propagation (BP). Since BP provides precise posteriorinformation using the noise statistic, BHT-BP can recover the support withrobustness against the measurement noise. In addition, BHT-BP has lowcomputational cost compared to the other algorithms by the use of BP. We showthe support recovery performance of BHT-BP on the parameters (N; M; K; SNR) andcompare the performance of BHT-BP to OMP and Lasso via numerical results.

Bayesian Hypothesis Test for Sparse Support Recovery using Belief Propagation