conditionLatentObs

<code>conditionLatentObs</code> assigns q_y(i) vs q_z(i) following Section 5.1 
in Katzfuss and Guinness (2018). This function only needs to be run once 
per SGV analysis.

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

conditionLatentObs function

<dl><dt>nID</dt>
<dd>N x k matrix of neighbor indices.</dd>
<dt>coords_ord</dt>
<dd>N x 2 matrix of locations.</dd>
<dt>N</dt>
<dd>Scalar; number of locations (observed only!).</dd></dl>

Arguments

Assign conditioning sets for the SGV approximation — conditionLatentObs

<dl>

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


<dt>coords_ord</dt>
<dd>N x 2 matrix of locations.</dd>


<dt>N</dt>
<dd>Scalar; number of locations (observed only!).</dd>

</dl>

conditionLatentObs: Assign conditioning sets for the SGV approximation

Description

Usage

Value

Arguments