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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A model-independent or non-parametric approach for modeling a database hasbeen widely used in cosmology. In these scenarios, the data has been useddirectly to reconstruct an underlying function. In this work, we introduce anovel semi-model-independent method to do the task. The new approach not onlyremoves some drawbacks of previous methods but also has some remarkableadvantages. We combine the well-known Gaussian linear model with a neuralnetwork and introduce a procedure for the reconstruction of an arbitraryfunction. In the scenario, the neural network produces some arbitrary basefunctions which subsequently are fed to the Gaussian linear model. Given aprior distribution on the free parameters, the Gaussian linear model provides aclose form for the posterior distribution as well as the Bayesian evidence. Inaddition, contrary to other methods, it is straightforward to compute theuncertainty.

A semi-model-independent approach to describe a cosmological database