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/

Zendy Plus

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

Zendy Tools

Zendy Open

This paper introduces a novel family of geostatistical models designed tocapture complex features beyond the reach of traditional Gaussian processes.The proposed family, termed the Poisson-Gaussian Mixture Process (POGAMP), ishierarchically specified, combining the infinite-dimensional dynamics ofGaussian processes with any multivariate continuous distribution. Thiscombination is stochastically defined by a latent Poisson process, allowing thePOGAMP to define valid processes with finite-dimensional distributions that canapproximate any continuous distribution. Unlike other non-Gaussiangeostatistical models that may fail to ensure validity of the processes byassigning arbitrary finite-dimensional distributions, the POGAMP preservesessential probabilistic properties crucial for both modeling and inference. Weestablish formal results regarding the existence and properties of the POGAMP,highlighting its robustness and flexibility in capturing complex spatialpatterns. To support practical applications, a carefully designed MCMCalgorithm is developed for Bayesian inference when the POGAMP is discretelyobserved over some spatial domain.

The Poisson-Gaussian Mixture Process: A Flexible and Robust Approach for Non-Gaussian Geostatistical Modeling