Description
Spatial Logistic Gaussian Process for Field Density Estimation.
Description
Provides tools for conditional and spatially dependent density estimation using Spatial Logistic Gaussian Processes (SLGPs). The approach represents probability densities through finite-rank Gaussian process priors transformed via a spatial logistic density transformation, enabling flexible non-parametric modeling of heterogeneous data. Functionality includes density prediction, quantile and moment estimation, sampling methods, and preprocessing routines for basis functions. Applications arise in spatial statistics, machine learning, and uncertainty quantification. The methodology builds on the framework of Leonard (1978) <doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988) <doi:10.1080/01621459.1988.10478625>, Tokdar (2007) <doi:10.1198/106186007X210206>, Tokdar (2010) <doi:10.1214/10-BA605>, and is further aligned with recent developments in Bayesian non-parametric modelling: see Gautier (2023) <https://boristheses.unibe.ch/4377/>, and Gautier (2025) <doi:10.48550/arXiv.2110.02876>).