evaluate_basis_functions

Evaluates all basis functions defined by a parameter list at new locations.

internal

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>).

Athénaïs Gautier

SLGP

Spatial Logistic Gaussian Process for Field Density Estimation

evaluate_basis_functions function

<dl><dt>parameters</dt>
<dd>List of basis function parameters.</dd>
<dt>X</dt>
<dd>Matrix or dataframe of evaluation locations.</dd>
<dt>lengthscale</dt>
<dd>Numeric vector. Lengthscales used for scaling the input space.</dd></dl>

Arguments

Evaluate basis functions at given locations. — evaluate_basis_functions

<dl>

<dt>parameters</dt>
<dd>List of basis function parameters.</dd>


<dt>X</dt>
<dd>Matrix or dataframe of evaluation locations.</dd>


<dt>lengthscale</dt>
<dd>Numeric vector. Lengthscales used for scaling the input space.</dd>

</dl>

evaluate_basis_functions: Evaluate basis functions at given locations.

Description

Usage

Value

Arguments