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

In this note we solve a general statistical inverse problem under absence ofknowledge of both the noise level and the noise distribution via application ofthe (modified) heuristic discrepancy principle. Hereby the unbounded(non-Gaussian) noise is controlled via introducing an auxiliary discretisationdimension and choosing it in an adaptive fashion. We first show convergence forcompletely arbitrary compact forward operator and ground solution. Then theuncertainty of reaching the optimal convergence rate is quantified in aspecific Bayesian-like environment.

Noise level free regularisation of general linear inverse problems under unconstrained white noise