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  2. Divergence (statistics) - Wikipedia

    en.wikipedia.org/wiki/Divergence_(statistics)

    Thus, a divergence D(·, ·) generates on a statistical manifold a unique dualistic structure (g (D), ∇ (D), ∇ (D*)). The converse is also true: every torsion-free dualistic structure on a statistical manifold is induced from some globally defined divergence function (which however need not be unique). [5]

  3. f-divergence - Wikipedia

    en.wikipedia.org/wiki/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 f {\displaystyle f} -divergence.

  4. Kullback–Leibler divergence - Wikipedia

    en.wikipedia.org/wiki/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 much a model probability distribution Q is different from a true probability distribution P.

  5. Bregman divergence - Wikipedia

    en.wikipedia.org/wiki/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.

  6. Hellinger distance - Wikipedia

    en.wikipedia.org/wiki/Hellinger_distance

    In probability and statistics, the Hellinger distance (closely related to, although different from, the Bhattacharyya distance) is used to quantify the similarity between two probability distributions. It is a type of f-divergence. The Hellinger distance is defined in terms of the Hellinger integral, which was introduced by Ernst Hellinger in 1909.

  7. Jensen–Shannon divergence - Wikipedia

    en.wikipedia.org/wiki/Jensen–Shannon_divergence

    Quantum Jensen–Shannon divergence for = (,) and two density matrices is a symmetric function, everywhere defined, bounded and equal to zero only if two density matrices are the same. It is a square of a metric for pure states , [ 13 ] and it was recently shown that this metric property holds for mixed states as well.

  8. Divergence - Wikipedia

    en.wikipedia.org/wiki/Divergence

    More technically, the divergence represents the volume density of the outward flux of a vector field from an infinitesimal volume around a given point. As an example, consider air as it is heated or cooled. The velocity of the air at each point defines a vector field. While air is heated in a region, it expands in all directions, and thus the ...

  9. Convergence tests - Wikipedia

    en.wikipedia.org/wiki/Convergence_tests

    While most of the tests deal with the convergence of infinite series, they can also be used to show the convergence or divergence of infinite products. This can be achieved using following theorem: Let { a n } n = 1 ∞ {\displaystyle \left\{a_{n}\right\}_{n=1}^{\infty }} be a sequence of positive numbers.