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

The Indian buffet process (IBP) and phylogenetic Indian buffet process (pIBP)can be used as prior models to infer latent features in a data set. Thetheoretical properties of these models are under-explored, however, especiallyin high dimensional settings. In this paper, we show that under mild sparsitycondition, the posterior distribution of the latent feature matrix, generatedvia IBP or pIBP priors, converges to the true latent feature matrixasymptotically. We derive the posterior convergence rate, referred to as thecontraction rate. We show that the convergence holds even when thedimensionality of the latent feature matrix increases with the sample size,therefore making the posterior inference valid in high dimensional setting. Wedemonstrate the theoretical results using computer simulation, in which theparallel-tempering Markov chain Monte Carlo method is applied to overcomecomputational hurdles. The practical utility of the derived properties isdemonstrated by inferring the latent features in a reverse phase protein arrays(RPPA) dataset under the IBP prior model. Software and dataset reported in themanuscript are provided at http://www.compgenome.org/IBP.

Posterior Contraction Rate of Sparse Latent Feature Models with Application to Proteomics