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The total variation distance is half of the L 1 distance between the probability functions: on discrete domains, this is the distance between the probability mass functions [4] (,) = | () |, and when the distributions have standard probability density functions p and q, [5]
The total variation distance of two distributions and over a finite domain , (often referred to as statistical difference [2] or statistical distance [3] in ...
The following result, due to Donsker and Varadhan, [29] is known as Donsker and Varadhan's variational formula Let be a ... The total-variation distance, ...
A metric or distance function is a function d which takes pairs of points or objects to real numbers and satisfies the following rules: The distance between an object and itself is always zero. The distance between distinct objects is always positive. Distance is symmetric: the distance from x to y is always the same as the distance from y to x.
Braking distance refers to the distance a vehicle will travel from the point when its brakes are fully applied to when it comes to a complete stop. It is primarily affected by the original speed of the vehicle and the coefficient of friction between the tires and the road surface, [Note 1] and negligibly by the tires' rolling resistance and vehicle's air drag.
In mathematics, the total variation identifies several slightly different concepts, related to the (local or global) structure of the codomain of a function or a measure.For a real-valued continuous function f, defined on an interval [a, b] ⊂ R, its total variation on the interval of definition is a measure of the one-dimensional arclength of the curve with parametric equation x ↦ f(x ...
In mathematics, the Wasserstein distance or Kantorovich–Rubinstein metric is a distance function defined between probability distributions on a given metric space. It is named after Leonid Vaseršteĭn .
The distance formula itself was first published in 1731 by Alexis Clairaut. [33] Because of this formula, Euclidean distance is also sometimes called Pythagorean ...