logLikHessian

Hessian of the log-likelihood with respect to lengthscales hyperparameters
Works for homGP and hetGP models from the hetGP package for now.

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

logLikHessian function

<dl><dt>model</dt>
<dd>homGP model</dd></dl>

Arguments

Hessian of the log-likelihood with respect to lengthscales hyperparameters
Works for homGP and hetGP models from the hetGP package for now. — logLikHessian

<dl>

<dt>model</dt>
<dd>homGP model</dd>

</dl>

logLikHessian: Hessian of the log-likelihood with respect to lengthscales hyperparameters Works for homGP and hetGP models from the hetGP package for now.

Description

Usage

Value

Arguments