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 problem of detecting a few anomalous processes among a large number ofdata streams is considered. At each time, aggregated observations can be takenfrom a chosen subset of the processes, where the chosen subset conforms to agiven tree structure. The random observations are drawn from a generaldistribution that may depend on the size of the chosen subset and the number ofanomalous processes in the subset. We propose a sequential search strategy bydevising an information-directed random walk (IRW) on the tree-structuredobservation hierarchy. Subject to a reliability constraint, the proposed policyis shown to be asymptotically optimal with respect to the detection accuracy.Furthermore, it achieves the optimal logarithmic-order sample complexity withrespect to the size of the search space provided that the Kullback-Leiblerdivergence between aggregated observations in the presence and the absence ofanomalous processes are bounded away from zero at all levels of the treestructure as the size of the search space approaches infinity. Sufficientconditions on the decaying rate of the aggregated observations to pure noiseunder which a sublinear scaling in the size of the search space is preservedare also identified for the Bernoulli case.

Information-Directed Random Walk for Rare Event Detection in Hierarchical Processes