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

This paper addresses the problem of summarizing the posterior distributionsthat typically arise, in a Bayesian framework, when dealing with signaldecomposition problems with unknown number of components. Such posteriordistributions are defined over union of subspaces of differing dimensionalityand can be sampled from using modern Monte Carlo techniques, for instance theincreasingly popular RJ-MCMC method. No generic approach is available, however,to summarize the resulting variable-dimensional samples and extract from themcomponent-specific parameters.  We propose a novel approach to this problem, which consists in approximatingthe complex posterior of interest by a "simple"---but stillvariable-dimensional---parametric distribution. The distance between the twodistributions is measured using the Kullback-Leibler divergence, and aStochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed toestimate the parameters. The proposed algorithm is illustrated on thefundamental signal processing example of joint detection and estimation ofsinusoids in white Gaussian noise.

Summarizing posterior distributions in signal decomposition problems when the number of components is unknown