R_sparse_crossprod

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_crossprod 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>
<dt>z</dt>
<dd>Vector to calculate the cross-product with.</dd>
<dt>n</dt>
<dd>Length of the vector</dd>
<dt>subset</dt>
<dd>Optional vector of rows to include in the calculation.</dd>
<dt>transp</dt>
<dd>Optional indicator of using the transpose</dd></dl>

Arguments

nimble_sparse_crossprod — R_sparse_crossprod

<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>


<dt>z</dt>
<dd>Vector to calculate the cross-product with.</dd>


<dt>n</dt>
<dd>Length of the vector</dd>


<dt>subset</dt>
<dd>Optional vector of rows to include in the calculation.</dd>


<dt>transp</dt>
<dd>Optional indicator of using the transpose</dd>

</dl>

R_sparse_crossprod: nimble_sparse_crossprod

Description

Usage

Arguments