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 adaptive sampling for estimating probability mass functions(pmf) uniformly well is considered. Performance of the sampling strategy ismeasured in terms of the worst-case mean squared error. A Bayesian variant ofthe existing upper confidence bound (UCB) based approaches is proposed. It isshown analytically that the performance of this Bayesian variant is no worsethan the existing approaches. The posterior distribution on the pmfs in theBayesian setting allows for a tighter computation of upper confidence boundswhich leads to significant performance gains in practice. Using this approach,adaptive sampling protocols are proposed for estimating SARS-CoV-2seroprevalence in various groups such as location and ethnicity. Theeffectiveness of this strategy is discussed using data obtained from aseroprevalence survey in Los Angeles county.

Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach