rspdnorm

Simulates random samples from a Riemannian normal distribution on symmetric positive definite matrices.

Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (<doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (<doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (<doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.

Nicolas Escobar

riemtan

Riemannian Metrics for Symmetric Positive Definite Matrices

Jaroslaw Harezlak

rspdnorm function

<dl><dt>n</dt>
<dd>Number of samples to generate.</dd>
<dt>refpt</dt>
<dd>Reference point on the manifold, represented as a symmetric positive definite matrix. Must be an object of class <code>dppMatrix</code> from the Matrix package.</dd>
<dt>disp</dt>
<dd>Dispersion matrix defining the spread of the distribution. Must be an object of class <code>dppMatrix</code> from the Matrix package.</dd>
<dt>met</dt>
<dd>A metric object of class <code>rmetric</code>.</dd></dl>

Arguments

Generate Random Samples from a Riemannian Normal Distribution — rspdnorm

<dl>

<dt>n</dt>
<dd>Number of samples to generate.</dd>


<dt>refpt</dt>
<dd>Reference point on the manifold, represented as a symmetric positive definite matrix. Must be an object of class <code>dppMatrix</code> from the Matrix package.</dd>


<dt>disp</dt>
<dd>Dispersion matrix defining the spread of the distribution. Must be an object of class <code>dppMatrix</code> from the Matrix package.</dd>


<dt>met</dt>
<dd>A metric object of class <code>rmetric</code>.</dd>

</dl>

rspdnorm: Generate Random Samples from a Riemannian Normal Distribution

Description

Usage

Value

Arguments