W_kappa_ij_up

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

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

W_kappa_ij_up function

<dl><dt>W</dt>
<dd>The matrix to store the computation in</dd>
<dt>design</dt>
<dd>matrix of design points</dd>
<dt>theta</dt>
<dd>lengthscales</dd>
<dt>i1, i2</dt>
<dd>index of the derivatives (WARNING: starts at 0)</dd>
<dt>start</dt>
<dd>The column/row index at which to start the computation (doesn't touch the start by start submatrix).</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 — W_kappa_ij_up

<dl>

<dt>W</dt>
<dd>The matrix to store the computation in</dd>


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


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


<dt>i1, i2</dt>
<dd>index of the derivatives (WARNING: starts at 0)</dd>


<dt>start</dt>
<dd>The column/row index at which to start the computation (doesn't touch the start by start submatrix).</dd>


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

</dl>

W_kappa_ij_up: Covariance of kernel computations

Description

Usage

Value

Arguments