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  2. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    If the points in the joint probability distribution of X and Y that receive positive probability tend to fall along a line of positive (or negative) slope, ρ XY is near +1 (or −1). If ρ XY equals +1 or −1, it can be shown that the points in the joint probability distribution that receive positive probability fall exactly along a straight ...

  3. Joint distribution - Wikipedia

    en.wikipedia.org/?title=Joint_distribution&...

    Download as PDF; Printable version; ... move to sidebar hide. From Wikipedia, the free encyclopedia. Redirect page. Redirect to: Joint probability distribution;

  4. Template:Probability distributions - Wikipedia

    en.wikipedia.org/wiki/Template:Probability...

    Template: Probability distributions. ... Download as PDF; Printable version; ... This page was last edited on 18 December 2024, ...

  5. Copula (statistics) - Wikipedia

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

    In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe/model the dependence (inter-correlation) between random variables . [ 1 ]

  6. Template:Probability fundamentals - Wikipedia

    en.wikipedia.org/wiki/Template:Probability...

    Part of a series on statistics: Probability theory; Probability. Axioms; Determinism. System; Indeterminism; Randomness; Probability space; Sample space; Event ...

  7. Bapat–Beg theorem - Wikipedia

    en.wikipedia.org/wiki/Bapat–Beg_theorem

    In probability theory, the Bapat–Beg theorem gives the joint probability distribution of order statistics of independent but not necessarily identically distributed random variables in terms of the cumulative distribution functions of the random variables. Ravindra Bapat and M.I. Beg published the theorem in 1989, [1] though they did not ...

  8. Generative model - Wikipedia

    en.wikipedia.org/wiki/Generative_model

    One can compute this directly, without using a probability distribution (distribution-free classifier); one can estimate the probability of a label given an observation, (| =) (discriminative model), and base classification on that; or one can estimate the joint distribution (,) (generative model), from that compute the conditional probability ...

  9. Exchangeable random variables - Wikipedia

    en.wikipedia.org/wiki/Exchangeable_random_variables

    Formally, an exchangeable sequence of random variables is a finite or infinite sequence X 1, X 2, X 3, ... of random variables such that for any finite permutation σ of the indices 1, 2, 3, ..., (the permutation acts on only finitely many indices, with the rest fixed), the joint probability distribution of the permuted sequence