gaussian_fun: Multivariate Gaussian linear model evaluation

In gsaot: Compute Global Sensitivity Analysis Indices Using Optimal Transport

Multivariate Gaussian linear model evaluation

Description

Generates samples from a multivariate Gaussian distribution and evaluates a simple linear transformation model.

Usage

gaussian_fun(N)

Arguments

N	Number of input samples to generate.

Details

Inputs x are sampled from:

\mathbf{X} \sim \mathcal{N}(\boldsymbol{\mu}, \Sigma), \quad \boldsymbol{\mu} = [1, 1, 1], \quad \Sigma = \begin{bmatrix} 1 & 0.5 & 0.5 \\ 0.5 & 1 & 0.5 \\ 0.5 & 0.5 & 1 \end{bmatrix}

The output is given by:

\mathbf{Y} = A \mathbf{X}^{\top}, \quad A = \begin{bmatrix} 4 & -2 & 1 \\ 2 & 5 & -1 \end{bmatrix}

Value

A list with two elements:

• x: a numeric matrix of size N x 8 containing the input samples.

• y: a numeric vector of length N with the corresponding function outputs.

See Also

sobol_fun, ishi_homma_fun

Examples

result <- gaussian_fun(1000)
head(result$x)
head(result$y)

gsaot documentation built on Aug. 8, 2025, 7:52 p.m.