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KL divergence between two multivariate Gaussians

version 1.0.2 (1.67 KB) by Statovic
Function to efficiently compute the Kullback-Leibler divergence between two multivariate Gaussian distributions.

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Updated 26 Feb 2021

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This function computes the Kullback-Leibler (KL) divergence between two multivariate Gaussian distributions with specified parameters (mean and covariance matrix). The covariance matrices must be positive definite. The code is efficient and numerically stable.

Examples:
1) Compute the KL divergence between two univariate Gaussians: KL( N(-1,1) || N(+1,1) )
mu1 = -1; mu = +1;
s1 = 1; s2 = 1;
mvgkl(mu1, s1^2, mu2, s2^2)

2) Compute the KL divergence between two bivariate Gaussians: KL( N(mu1,S1) || N(mu2,S2) )
mu1 = [-1 -1]'; mu2 = [+1, +1]';
S1 = [1 0.5; 0.5 1]; S2 = [1 -0.7; -0.7 1];
mvgkl(mu1, S1, mu2, S2)

Cite As

Statovic (2021). KL divergence between two multivariate Gaussians (https://www.mathworks.com/matlabcentral/fileexchange/87899-kl-divergence-between-two-multivariate-gaussians), MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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