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  2. Divergence (statistics) - Wikipedia

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

    The only divergence for probabilities over a finite alphabet that is both an f-divergence and a Bregman divergence is the Kullback–Leibler divergence. [8] The squared Euclidean divergence is a Bregman divergence (corresponding to the function ⁠ x 2 {\displaystyle x^{2}} ⁠ ) but not an f -divergence.

  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. Bregman divergence - Wikipedia

    en.wikipedia.org/wiki/Bregman_divergence

    The only divergence on that is both a Bregman divergence and an f-divergence is the Kullback–Leibler divergence. [ 6 ] If n ≥ 3 {\displaystyle n\geq 3} , then any Bregman divergence on Γ n {\displaystyle \Gamma _{n}} that satisfies the data processing inequality must be the Kullback–Leibler divergence.

  5. 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.

  6. 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.

  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. Solenoidal vector field - Wikipedia

    en.wikipedia.org/wiki/Solenoidal_vector_field

    An example of a solenoidal vector field, (,) = (,) In vector calculus a solenoidal vector field (also known as an incompressible vector field , a divergence-free vector field , or a transverse vector field ) is a vector field v with divergence zero at all points in the field: ∇ ⋅ v = 0. {\displaystyle \nabla \cdot \mathbf {v} =0.}