I have to compute the Wasserstein distance between two bivariate Gaussian distributions with means , and covariances , . According to equation 9 ofthis paper, in the Gaussian case the Wasserstein distance admits the following analytic expression ...
Large ball probabilities, Gaussian comparison and anti-concentration We derive tight non-asymptotic bounds for the Kolmogorov distance between the probabilities of two Gaussian elements to hit a ball in a Hilbert space. The ... F Gtze,A Naumov,V Spokoiny,... - Bernoulli Society for Mathematica...
In this paper we show that the bootstrap approximates consistently the distribution of the sample Matusita distance between two conditional Gaussian distributions, under the null hypothesis of homogeneity. We also study by simulation the finite sample performance of the bootstrap distribution and ...
That is, the test statistic is based on an estimate of the Kolmogorov distance between the two distributions. Let Zi be the n + m pooled observations. In symbols, Zi = Xi, i = 1,…, n, and Zn+i = Yi, i = 1,…, m. Then the Kolmogorov–Smirnov test statistic is (5.4)D=max...
In this paper, we introduce a Bhattacharyya-based GMM-distance to measure the distance between two GMM distributions. Subsequently, the GMM-UBM mean interval (GUMI) concept is introduced to derive a GUMI kernel which can be used in conjunction with support vector machine (SVM) for speaker ...
State space evaluation of the Bhattacharyya distance between two Gaussian processes One measure of the distance between two Gaussian processes is the Bhattacharyya distance. State space techniques are used to express this measure in terms ... FC Schweppe - 《Information & Control》 被引量: 30发表:...
A much more tractable distance between probability laws is given by the Wasserstein–Kantorovich–Rubinstein distance, which is based on the optimal transport (or coupling) between two given distributions, see [59, 60]. In [61], for instance, the authors study (abstract) WKR perturbations of ...
Based on these ideas, we suggest two novel algorithms: one to prune Gaussian mixtures (GMs) and the other to perform a weighted sampling of GMs. Finally, we compare the tracking quality between identical trackers where GM pruning is done with the suggested and the conventional algorithms. 展开...
Hi, I am trying to compute the (bures) wasserstein distance between gaussians of different dimensions. Mainly I was checking on the tutorial shown here where they try to find couplings of datasets with different sizes but of the same dim...
where \(D_{i}\) represents the distance between the ith individual and the best individual, \(F_{i}\) is fitness value, \(S_{i}\) denotes the score of the ith individual, \(\omega \) represents the functional weight, and \(\omega \) is randomly generated by Gaussian distribution...