quick_C

Computes Int(kappa_i(X, design) . kappa_j(design, X)). This function is preferred for initialization

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

quick_C function

<dl><dt>measure</dt>
<dd>An integer giving the measure of integration. 0 for Lebesgue/Uniform on [0,1]^m, 1 for (truncated) Gaussian on [0,1]^m, 2 for Gaussian on R^m.</dd>
<dt>design</dt>
<dd>matrix of design points</dd>
<dt>Ki</dt>
<dd>The inverse covariance matrix</dd>
<dt>Kir</dt>
<dd>The inverse covariance matrix times the response.</dd>
<dt>theta</dt>
<dd>lengthscales</dd>
<dt>xm</dt>
<dd>The mean vector associated with the Gaussian measure. Ignored if uniform.</dd>
<dt>xv</dt>
<dd>The variance vector associated with the Gaussian measure (diagonal of covariance matrix, vars assumed independent). Ignored if uniform.</dd>
<dt>ct</dt>
<dd>Covariance type, 1 means Gaussian, 2 means Matern 3/2, 3 means Matern 5/2</dd></dl>

Arguments

Covariance of kernel computations — quick_C

<dl>

<dt>measure</dt>
<dd>An integer giving the measure of integration. 0 for Lebesgue/Uniform on [0,1]^m, 1 for (truncated) Gaussian on [0,1]^m, 2 for Gaussian on R^m.</dd>


<dt>design</dt>
<dd>matrix of design points</dd>


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


<dt>Kir</dt>
<dd>The inverse covariance matrix times the response.</dd>


<dt>theta</dt>
<dd>lengthscales</dd>


<dt>xm</dt>
<dd>The mean vector associated with the Gaussian measure. Ignored if uniform.</dd>


<dt>xv</dt>
<dd>The variance vector associated with the Gaussian measure (diagonal of covariance matrix, vars assumed independent). Ignored if uniform.</dd>


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

</dl>

quick_C: Covariance of kernel computations

Description

Usage

Value

Arguments