W_kappa_lk

Creates a submatrix of the same tensor as W_kappa_ij, but this time l,k give the indices of the observations, not variables.

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_lk function

<dl><dt>design</dt>
<dd>matrix of design points</dd>
<dt>theta</dt>
<dd>lengthscales</dd>
<dt>i1, i2</dt>
<dd>index of the observations (WARNING: starts at 0)</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_lk

<dl>

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


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


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


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

</dl>

W_kappa_lk: Covariance of kernel computations

Description

Usage

Value

Arguments