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  2. Kullback–Leibler divergence - Wikipedia

    en.wikipedia.org/wiki/Kullback–Leibler_divergence

    A simple interpretation of the KL divergence of P from Q is the expected excess surprise from using Q as a model instead of P when the actual distribution is P. While it is a measure of how different two distributions are, and in some sense is thus a "distance", it is not actually a metric , which is the most familiar and formal type of distance.

  3. Koutecký–Levich equation - Wikipedia

    en.wikipedia.org/wiki/Koutecký–Levich_equation

    B L is the Levich Constant. ω is the angular rotation rate of the electrode (rad/s) From an experimental data set where the current is measured at different rotation rates, it is possible to extract the kinetic current from a so-called Koutecký–Levich plot.

  4. Mutual information - Wikipedia

    en.wikipedia.org/wiki/Mutual_information

    where is the Kullback–Leibler divergence, and is the outer product distribution which assigns probability () to each (,).. Notice, as per property of the Kullback–Leibler divergence, that (;) is equal to zero precisely when the joint distribution coincides with the product of the marginals, i.e. when and are independent (and hence observing tells you nothing about ).

  5. Total variation distance of probability measures - Wikipedia

    en.wikipedia.org/wiki/Total_variation_distance...

    The total variation distance (or half the norm) arises as the optimal transportation cost, when the cost function is (,) =, that is, ‖ ‖ = (,) = {(): =, =} = ⁡ [], where the expectation is taken with respect to the probability measure on the space where (,) lives, and the infimum is taken over all such with marginals and , respectively.

  6. f-divergence - Wikipedia

    en.wikipedia.org/wiki/F-divergence

    Many common divergences, such as KL-divergence, Hellinger distance, and total variation distance, are special cases of ... Plugging the formula into () = () ...

  7. Hellinger distance - Wikipedia

    en.wikipedia.org/wiki/Hellinger_distance

    To define the Hellinger distance in terms of elementary probability theory, we take λ to be the Lebesgue measure, so that dP / dλ and dQ / dλ are simply probability density functions.

  8. Kosambi–Karhunen–Loève theorem - Wikipedia

    en.wikipedia.org/wiki/Kosambi–Karhunen–Loève...

    The covariance function K X satisfies the definition of a Mercer kernel. By Mercer's theorem, there consequently exists a set λ k, e k (t) of eigenvalues and eigenfunctions of T K X forming an orthonormal basis of L 2 ([a,b]), and K X can be expressed as

  9. Taylor knock-out factor - Wikipedia

    en.wikipedia.org/wiki/Taylor_knock-out_factor

    Taylor himself acknowledged this, stating "in the case of soft-skinned non-dangerous game, such as is generally shot at medium to long ranges, theoretical mathematical energy may possibly prove a more reliable guide" and that his formula was designed to measure a cartridge's performance against the large, thick skinned, big boned elephant. [4]