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  2. Pair distribution function - Wikipedia

    en.wikipedia.org/wiki/Pair_distribution_function

    The pair distribution function describes the distribution of distances between pairs of particles contained within a given volume. [1] Mathematically, if a and b are two particles, the pair distribution function of b with respect to a, denoted by () is the probability of finding the particle b at distance from a, with a taken as the origin of coordinates.

  3. Independence (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Independence_(probability...

    Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.

  4. Pairwise independence - Wikipedia

    en.wikipedia.org/wiki/Pairwise_independence

    Pairwise independent random variables with finite variance are uncorrelated. A pair of random variables X and Y are independent if and only if the random vector (X, Y) with joint cumulative distribution function (CDF) , (,) satisfies , (,) = (),

  5. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables and the conditional probability distributions, which deal with how the outputs of one random variable are distributed when given information on the outputs of the other random variable(s).

  6. Exchangeable random variables - Wikipedia

    en.wikipedia.org/wiki/Exchangeable_random_variables

    Partition the sequence into non-overlapping pairs: if the two elements of the pair are equal (00 or 11), discard it; if the two elements of the pair are unequal (01 or 10), keep the first. This yields a sequence of Bernoulli trials with p = 1 / 2 , {\displaystyle p=1/2,} as, by exchangeability, the odds of a given pair being 01 or 10 are equal.

  7. 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 ).

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  9. Pairwise comparison - Wikipedia

    en.wikipedia.org/wiki/Pairwise_comparison

    Download as PDF; Printable version; In other projects ... Appearance. move to sidebar hide. Pairwise comparison may refer to: Pairwise comparison (psychology) ...