torch (version 0.9.0)

distr_multivariate_normal: Gaussian distribution

Description

Creates a multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix.

Usage

distr_multivariate_normal(
  loc,
  covariance_matrix = NULL,
  precision_matrix = NULL,
  scale_tril = NULL,
  validate_args = NULL
)

Arguments

loc

(Tensor): mean of the distribution

covariance_matrix

(Tensor): positive-definite covariance matrix

precision_matrix

(Tensor): positive-definite precision matrix

scale_tril

(Tensor): lower-triangular factor of covariance, with positive-valued diagonal

validate_args

Bool wether to validate the arguments or not.

Details

The multivariate normal distribution can be parameterized either in terms of a positive definite covariance matrix $\mathbf{\Sigma }$ or a positive definite precision matrix ${\mathbf{\Sigma }}^{-1}$ or a lower-triangular matrix $\mathbf{L}$ with positive-valued diagonal entries, such that $\mathbf{\Sigma }=\mathbf{L}{\mathbf{L}}^{\mathrm{\top }}$. This triangular matrix can be obtained via e.g. Cholesky decomposition of the covariance.

See Also

Distribution for details on the available methods.

Other distributions: distr_bernoulli(), distr_chi2(), distr_gamma(), distr_normal(), distr_poisson()

Examples

if (torch_is_installed()) {
m <- distr_multivariate_normal(torch_zeros(2), torch_eye(2))
m$sample() # normally distributed with mean=`[0,0]` and covariance_matrix=`I`
}