simu_kldiv

Given a target density and a kernel estimator, evaluate the
Kullback-Leibler divergence by Monte Carlo integration by simulating draws from the corresponding model.

internal

Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.

Leo Belzile

Kernel Density Estimation for Random Symmetric Positive Definite
Matrices

Frederic Ouimet

simu_kldiv function

<dl><dt>x</dt>
<dd>a cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the sample matrices which define the kernel matrix estimator</dd>
<dt>b</dt>
<dd>positive double, bandwidth parameter</dd>
<dt>model</dt>
<dd>integer between 1 and 6 indicating the simulation scenario</dd>
<dt>B</dt>
<dd>number of Monte Carlo replications, default to 10K</dd></dl>

Arguments

Kullback-Leibler divergence via Monte Carlo — simu_kldiv

<dl>

<dt>x</dt>
<dd>a cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the sample matrices which define the kernel matrix estimator</dd>


<dt>b</dt>
<dd>positive double, bandwidth parameter</dd>


<dt>model</dt>
<dd>integer between 1 and 6 indicating the simulation scenario</dd>


<dt>B</dt>
<dd>number of Monte Carlo replications, default to 10K</dd>

</dl>

simu_kldiv: Kullback-Leibler divergence via Monte Carlo

Description

Usage

Value

Arguments