# There is a mistake in the paper. We present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. If two distributions are identical, their KL div. KL Divergence | Datumorphism | L Ma I need to determine the KL-divergence between two Gaussians. Share. Compute KL (Kullback–Leibler) Divergence Between Two … If we have two probability distributions, P and Q, we typically write the KL divergence using the notation KL(P || Q), which means “P’s divergence from Q.” We calculate it using the following formula: Yufeng … This sounds to me like a multivariate gaussian KL divergence problem, so I looked at the formula and I noticed that I actually need the covariance matrix of q (if we assume that KL(p||q)). What if we go a bit further off-piste: let’s consider the Kullback-Leibler divergence between two arbitrary N-dimensional probability distributions and :. For Gaussian … We present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. The KL Divergence is a measure of the dissimilarity between a ‘true’ distribution and a ‘prediction’ distribution. In this paper, we investigate the properties of KL divergence between Gaussians. The below GIF shows the optimization of the KL-divergence between distribution 1 (mixture of Gaussians) and distribution 2 (Gaussian) G5: Approximating the KL-divergence G6: Implementing variational inference for linear regression