Search results
Results From The WOW.Com Content Network
In statistics, probability theory, and information theory, a statistical distance quantifies the distance between two statistical objects, which can be two random variables, or two probability distributions or samples, or the distance can be between an individual sample point and a population or a wider sample of points. A distance between ...
In probability theory, the total variation distance is a distance measure for probability distributions. It is an example of a statistical distance metric, and is sometimes called the statistical distance , statistical difference or variational distance .
In probability theory, integral probability metrics are types of distance functions between probability distributions, defined by how well a class of functions can distinguish the two distributions. Many important statistical distances are integral probability metrics, including the Wasserstein-1 distance and the total variation distance .
Download as PDF; Printable version ... denotes a distance between means of X and Y. Example. For example if both probability distribution functions of random ...
4 As a distance between probability distributions (the FID score) 5 Variants. 6 Examples. 7 Applications. ... Download as PDF; Printable version;
In information geometry, the Fisher information metric [1] is a particular Riemannian metric which can be defined on a smooth statistical manifold, i.e., a smooth manifold whose points are probability distributions. It can be used to calculate the distance between probability distributions. [2] The metric is interesting in several aspects.
In probability theory, an -divergence is a certain type of function (‖) that measures the difference between two probability distributions and . Many common divergences, such as KL-divergence , Hellinger distance , and total variation distance , are special cases of f {\displaystyle f} -divergence.
In computer science, for example, the metric W 1 is widely used to compare discrete distributions, e.g. the color histograms of two digital images; see earth mover's distance for more details. In their paper ' Wasserstein GAN ', Arjovsky et al. [ 5 ] use the Wasserstein-1 metric as a way to improve the original framework of generative ...