getMatCovFromFactorCpp

Calculate the covariance values required by HV for
matrix factors passed as sparse matrices

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

Marcin Jurek

GPvecchia

Scalable Gaussian-Process Approximations

Matthias Katzfuss

Daniel Zilber

Wenlong Gong

Joe Guinness

Jingjie Zhang

Florian Schaefer

getMatCovFromFactorCpp function

<dl><dt>F</dt>
<dd>factor of a matrix in a sparse format</dd>
<dt>revNNarray</dt>
<dd>array with the neighbourhood structure</dd></dl>

Arguments

Calculate the covariance values required by HV for
matrix factors passed as sparse matrices — getMatCovFromFactorCpp

<dl>

<dt>F</dt>
<dd>factor of a matrix in a sparse format</dd>


<dt>revNNarray</dt>
<dd>array with the neighbourhood structure</dd>

</dl>

getMatCovFromFactorCpp: Calculate the covariance values required by HV for matrix factors passed as sparse matrices

Description

Usage

Value

Arguments