dmnorm_nngp

<code>dmnorm_nngp</code> (and <code>rmnorm_nngp</code>) calculate the approximate NNGP
likelihood for a fixed set of parameters (i.e., A and D 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

dmnorm_nngp function

<dl><dt>x</dt>
<dd>N-vector of data.</dd>
<dt>mean</dt>
<dd>N-vector with current values of the mean</dd>
<dt>AD</dt>
<dd>N x (k+1) matrix; the first k columns are the 'A' matrix, and the
last column is the 'D' vector.</dd>
<dt>nID</dt>
<dd>N x k matrix of neighbor indices.</dd>
<dt>N</dt>
<dd>Scalar; number of data measurements.</dd>
<dt>k</dt>
<dd>Scalar; number of nearest neighbors.</dd>
<dt>log</dt>
<dd>Scalar; should the density be on the log scale (1) or not (0).</dd></dl>

Arguments

Function for the evaluating the NNGP approximate density. — dmnorm_nngp

<dl>

<dt>x</dt>
<dd>N-vector of data.</dd>


<dt>mean</dt>
<dd>N-vector with current values of the mean</dd>


<dt>AD</dt>
<dd>N x (k+1) matrix; the first k columns are the 'A' matrix, and the
last column is the 'D' vector.</dd>


<dt>nID</dt>
<dd>N x k matrix of neighbor indices.</dd>


<dt>N</dt>
<dd>Scalar; number of data measurements.</dd>


<dt>k</dt>
<dd>Scalar; number of nearest neighbors.</dd>


<dt>log</dt>
<dd>Scalar; should the density be on the log scale (1) or not (0).</dd>

</dl>

dmnorm_nngp: Function for the evaluating the NNGP approximate density.

Description

Usage

Value

Arguments