C_GP_cpp

Equivalent of <code>C_GP</code> using RcppArmadillo

internal

The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (JCGS, 2021) <doi:10.48550/arXiv.1907.11572>.

Nathan Wycoff

activegp

Gaussian Process Based Design and Analysis for the Active
Subspace Method

C_GP_cpp function

<dl><dt>design</dt>
<dd>A matrix of design points, one in each row</dd>
<dt>response</dt>
<dd>A vector of observations at each design point.</dd>
<dt>theta</dt>
<dd>vector of lengthscales</dd>
<dt>Ki</dt>
<dd>inverse covariance matrix</dd>
<dt>ct</dt>
<dd>Covariance type, 1 means Gaussian, 2 means Matern 3/2, 3 means Matern 5/2</dd></dl>

Arguments

Equivalent of C_GP using RcppArmadillo — C_GP_cpp

<dl>

<dt>design</dt>
<dd>A matrix of design points, one in each row</dd>


<dt>response</dt>
<dd>A vector of observations at each design point.</dd>


<dt>theta</dt>
<dd>vector of lengthscales</dd>


<dt>Ki</dt>
<dd>inverse covariance matrix</dd>


<dt>ct</dt>
<dd>Covariance type, 1 means Gaussian, 2 means Matern 3/2, 3 means Matern 5/2</dd>

</dl>

C_GP_cpp: Equivalent of `C_GP` using RcppArmadillo

Description

Usage

Value

Arguments