calcQF

<code>calcQF</code> calculates the quadratic form in the multivariate Gaussian 
based on the NNGP approximation, for a specific parameter combination. The
quadratic form is <code>t(u)C^{-1}v</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

calcQF function

<dl><dt>u</dt>
<dd>Vector; left product.</dd>
<dt>v</dt>
<dd>Vector; right product</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. Represents the Cholesky of <code>C^{-1}</code>.</dd>
<dt>nID</dt>
<dd>N x k matrix of neighbor indices.</dd></dl>

Arguments

Calculate the Gaussian quadratic form for the NNGP approximation — calcQF

<dl>

<dt>u</dt>
<dd>Vector; left product.</dd>


<dt>v</dt>
<dd>Vector; right product</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. Represents the Cholesky of <code>C^{-1}</code>.</dd>


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

</dl>

calcQF: Calculate the Gaussian quadratic form for the NNGP approximation

Description

Usage

Value

Arguments