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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. 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 ...

  4. Category:Types of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Category:Types_of...

    This page was last edited on 7 December 2016, at 21:06 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  5. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values [15] (almost surely) [16] which means that the probability of any event can be expressed as a (finite or countably infinite) sum: = (=), where is a countable set with () =.

  6. Category:Theory of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Category:Theory_of...

    Bahasa Indonesia; עברית ... Conditional probability distribution; Conditional variance; ... Joint probability distribution; K. Kernel embedding of distributions;

  7. Chain rule (probability) - Wikipedia

    en.wikipedia.org/wiki/Chain_rule_(probability)

    In probability theory, the chain rule [1] (also called the general product rule [2] [3]) describes how to calculate the probability of the intersection of, not necessarily independent, events or the joint distribution of random variables respectively, using conditional probabilities.

  8. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    In measure-theoretic probability theory, the density function is defined as the Radon–Nikodym derivative of the probability distribution relative to a common dominating measure. [5] The likelihood function is this density interpreted as a function of the parameter, rather than the random variable. [ 6 ]

  9. Joint distribution - Wikipedia

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

    This page was last edited on 6 December 2007, at 18:15 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.