# kldcauchy: Kullback-Leibler Divergence between Centered Multivariate... In mcauchyd: Multivariate Cauchy Distribution; Kullback-Leibler Divergence

 kldcauchy R Documentation

## Kullback-Leibler Divergence between Centered Multivariate Cauchy Distributions

### Description

Computes the Kullback-Leibler divergence between two random vectors distributed according to multivariate Cauchy distributions (MCD) with zero location vector.

### Usage

kldcauchy(Sigma1, Sigma2, eps = 1e-06)


### Arguments

 Sigma1 symmetric, positive-definite matrix. The scatter matrix of the first distribution. Sigma2 symmetric, positive-definite matrix. The scatter matrix of the second distribution. eps numeric. Precision for the computation of the partial derivative of the Lauricella D-hypergeometric function (see Details). Default: 1e-06.

### Details

Given X_1, a random vector of R^p distributed according to the MCD with parameters (0, \Sigma_1) and X_2, a random vector of R^p distributed according to the MCD with parameters (0, \Sigma_2).

Let \lambda_1, \dots, \lambda_p the eigenvalues of the square matrix \Sigma_1 \Sigma_2^{-1} sorted in increasing order:

\lambda_1 < \dots < \lambda_{p-1} < \lambda_p

Depending on the values of these eigenvalues, the computation of the Kullback-Leibler divergence of X_1 from X_2 is given by:

• if \lambda_1 < 1 and \lambda_p > 1:
 \displaystyle{ KL(X_1||X_2) = -\frac{1}{2} \ln{ \prod_{i=1}^p{\lambda_i}} + \frac{1+p}{2} \bigg( \ln{\lambda_p} }
 \displaystyle{ - \frac{\partial}{\partial a} \bigg\{ F_D^{(p)} \bigg( a, \underbrace{\frac{1}{2}, \dots, \frac{1}{2}, a + \frac{1}{2}}_p ; a + \frac{1+p}{2} ; 1 - \frac{\lambda_1}{\lambda_p}, \dots, 1 - \frac{\lambda_{p-1}}{\lambda_p}, 1 - \frac{1}{\lambda_p} \bigg) \bigg\}\bigg|_{a=0} \bigg) }

• if \lambda_p < 1:
 \displaystyle{ KL(X_1||X_2) = -\frac{1}{2} \ln{ \prod_{i=1}^p{\lambda_i}} }   \displaystyle{ - \frac{1+p}{2} \frac{\partial}{\partial a} \bigg\{ F_D^{(p)} \bigg( a, \underbrace{\frac{1}{2}, \dots, \frac{1}{2}}_p ; a + \frac{1+p}{2} ; 1 - \lambda_1, \dots, 1 - \lambda_p \bigg) \bigg\}\bigg|_{a=0} }

• if \lambda_1 > 1:
 \displaystyle{ KL(X_1||X_2) = -\frac{1}{2} \ln{ \prod_{i=1}^p{\lambda_i}} - \frac{1+p}{2} \prod_{i=1}^p\frac{1}{\sqrt{\lambda_i}} }
 \displaystyle{ \times \frac{\partial}{\partial a} \bigg\{ F_D^{(p)} \bigg( \frac{1+p}{2}, \underbrace{\frac{1}{2}, \dots, \frac{1}{2}}_p ; a + \frac{1+p}{2} ; 1 - \frac{1}{\lambda_1}, \dots, 1 - \frac{1}{\lambda_p} \bigg) \bigg\}\bigg|_{a=0} }

where F_D^{(p)} is the Lauricella D-hypergeometric function defined for p variables:

 \displaystyle{ F_D^{(p)}\left(a; b_1, ..., b_p; g; x_1, ..., x_p\right) = \sum\limits_{m_1 \geq 0} ... \sum\limits_{m_p \geq 0}{ \frac{ (a)_{m_1+...+m_p}(b_1)_{m_1} ... (b_p)_{m_p} }{ (g)_{m_1+...+m_p} } \frac{x_1^{m_1}}{m_1!} ... \frac{x_p^{m_p}}{m_p!} } }

The computation of the partial derivative uses the pochhammer function.

### Value

A numeric value: the Kullback-Leibler divergence between the two distributions, with two attributes attr(, "epsilon") (precision of the partial derivative of the Lauricella D-hypergeometric function,see Details) and attr(, "k") (number of iterations).

### Author(s)

Pierre Santagostini, Nizar Bouhlel

### References

N. Bouhlel, D. Rousseau, A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions. Entropy, 24, 838, July 2022. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/e24060838")}

### Examples


Sigma1 <- matrix(c(1, 0.6, 0.2, 0.6, 1, 0.3, 0.2, 0.3, 1), nrow = 3)
Sigma2 <- matrix(c(1, 0.3, 0.1, 0.3, 1, 0.4, 0.1, 0.4, 1), nrow = 3)
kldcauchy(Sigma1, Sigma2)
kldcauchy(Sigma2, Sigma1)

Sigma1 <- matrix(c(0.5, 0, 0, 0, 0.4, 0, 0, 0, 0.3), nrow = 3)
Sigma2 <- diag(1, 3)
# Case when all eigenvalues of Sigma1 %*% solve(Sigma2) are < 1
kldcauchy(Sigma1, Sigma2)
# Case when all eigenvalues of Sigma1 %*% solve(Sigma2) are > 1
kldcauchy(Sigma2, Sigma1)



mcauchyd documentation built on May 29, 2024, 2:21 a.m.