R_sparse_cholesky

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Daniel Turek

BayesNSGP

Bayesian Analysis of Non-Stationary Gaussian Process Models

Mark Risser

R_sparse_cholesky function

<dl><dt>i</dt>
<dd>Vector of row indices.</dd>
<dt>j</dt>
<dd>Vector of column indices.</dd>
<dt>x</dt>
<dd>Vector of values in the matrix.</dd></dl>

Arguments

R_sparse_chol — R_sparse_cholesky

<dl>

<dt>i</dt>
<dd>Vector of row indices.</dd>


<dt>j</dt>
<dd>Vector of column indices.</dd>


<dt>x</dt>
<dd>Vector of values in the matrix.</dd>

</dl>

R_sparse_cholesky: R_sparse_chol

Description

Usage

Arguments