This is a demo code for the unscented Hellinger distance between a pair of Gaussian mixture models. The code follows the derivation of the multivariate unscented Hellinger distance introduced in . Unlike the Kullback-Leibler divergence, the Hellinger distance is a proper metric between the distributions and is constrained to interval (0,1) with 0 meaning complete similarity and 1 complete dissimilarity.
 M. Kristan, A. Leonardis, D. Skočaj, "Multivariate online Kernel Density Estimation", Pattern Recognition, 2011. (url: http://vicos.fri.uni-lj.si/data/publications/KristanPR11.pdf)
Matej Kristan (2023). Unscented Hellinger distance between GMMs (https://www.mathworks.com/matlabcentral/fileexchange/36164-unscented-hellinger-distance-between-gmms), MATLAB Central File Exchange. Retrieved .
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