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

Probabilistic graphical models are graphical representations of probabilitydistributions. Graphical models have applications in many fields includingbiology, social sciences, linguistic, neuroscience. In this paper, we proposedirected acyclic graphs (DAGs) learning via bootstrap aggregating. The proposedprocedure is named as DAGBag. Specifically, an ensemble of DAGs is firstlearned based on bootstrap resamples of the data and then an aggregated DAG isderived by minimizing the overall distance to the entire ensemble. A family ofmetrics based on the structural hamming distance is defined for the space ofDAGs (of a given node set) and is used for aggregation. Under thehigh-dimensional-low-sample size setting, the graph learned on one data setoften has excessive number of false positive edges due to over-fitting of thenoise. Aggregation overcomes over-fitting through variance reduction and thusgreatly reduces false positives. We also develop an efficient implementation ofthe hill climbing search algorithm of DAG learning which makes the proposedmethod computationally competitive for the high-dimensional regime. The DAGBagprocedure is implemented in the R package dagbag.

Learning directed acyclic graphs via bootstrap aggregating