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Divergence (statistics) Function that measures dissimilarity between two probability distributions. In information geometry, a divergence is a kind of statistical distance: a binary function which establishes the separation from one probability distribution to another on a statistical manifold. The simplest divergence is squared Euclidean ...
f. -divergence. 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 -divergence.
Bregman divergence. In mathematics, specifically statistics and information geometry, a Bregman divergence or Bregman distance is a measure of difference between two points, defined in terms of a strictly convex function; they form an important class of divergences. When the points are interpreted as probability distributions – notably as ...
Kullback–Leibler divergence. In mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence[1]), denoted , is a type of statistical distance: a measure of how one reference probability distribution P is different from a second probability distribution Q. [2][3] Mathematically, it is defined as.
The Jensen–Shannon divergence (JSD) is a symmetrized and smoothed version of the Kullback–Leibler divergence . It is defined by. where is a mixture distribution of and . The geometric Jensen–Shannon divergence [7] (or G-Jensen–Shannon divergence) yields a closed-form formula for divergence between two Gaussian distributions by taking ...
The Elliott wave principle, or Elliott wave theory, is a form of technical analysis that helps financial traders analyze market cycles and forecast market trends by identifying extremes in investor psychology and price levels, such as highs and lows, by looking for patterns in prices. Ralph Nelson Elliott (1871–1948), an American accountant ...
Statistical distance. 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.
The Itakura–Saito distance (or Itakura–Saito divergence) is a measure of the difference between an original spectrum and an approximation of that spectrum. Although it is not a perceptual measure, it is intended to reflect perceptual (dis) similarity. It was proposed by Fumitada Itakura and Shuzo Saito in the 1960s while they were with NTT.