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

Spatially dependent data arises in many applications, and Gaussian processesare a popular modelling choice for these scenarios. While Bayesian analyses ofthese problems have proven to be successful, selecting prior distributions forthese complex models remains a difficult task. In this work, we propose aprincipled approach for setting prior distributions on model variancecomponents by placing a prior distribution on a measure of model fit. Inparticular, we derive the distribution of the prior coefficient ofdetermination. Placing a beta prior distribution on this measure induces ageneralized beta prime prior distribution on the global variance of the linearpredictor in the model. This method can also be thought of as shrinking the fittowards the intercept-only (null) model. We derive an efficient Gibbs samplerfor the majority of the parameters and use Metropolis-Hasting updates for theothers. Finally, the method is applied to a marine protection area data set. Weestimate the effect of marine policies on biodiversity and conclude thatno-take restrictions lead to a slight increase in biodiversity and that themajority of the variance in the linear predictor comes from the spatialeffect.\vspace{12pt}

Spatial regression modeling via the R2D2 framework