rmnorm_gp2Scale

<code>dmnorm_gp2Scale</code> (and <code>rmnorm_gp2Scale</code>) calculate the usual Gaussian
likelihood for a fixed set of parameters (but with sparse matrices). Finally,
the distributions must be registered within <code>nimble</code>.

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

rmnorm_gp2Scale function

<dl><dt>n</dt>
<dd>Number of realizations to generate</dd>
<dt>mean</dt>
<dd>Vector of mean values</dd>
<dt>Cov</dt>
<dd>Matrix of size N x N; sparse kernel</dd>
<dt>N</dt>
<dd>Number of measurements in x</dd>
<dt>Nnz</dt>
<dd>Number of measurements in x</dd></dl>

Arguments

Function for the evaluating the SGV approximate density. — rmnorm_gp2Scale

<dl>

<dt>n</dt>
<dd>Number of realizations to generate</dd>


<dt>mean</dt>
<dd>Vector of mean values</dd>


<dt>Cov</dt>
<dd>Matrix of size N x N; sparse kernel</dd>


<dt>N</dt>
<dd>Number of measurements in x</dd>


<dt>Nnz</dt>
<dd>Number of measurements in x</dd>

</dl>

rmnorm_gp2Scale: Function for the evaluating the SGV approximate density.

Description

Usage

Value

Arguments