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  2. Probability theory - Wikipedia

    en.wikipedia.org/wiki/Probability_theory

    Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations , probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms .

  3. Statistical model - Wikipedia

    en.wikipedia.org/wiki/Statistical_model

    A statistical model represents, often in considerably idealized form, the data-generating process. [1] When referring specifically to probabilities, the corresponding term is probabilistic model. All statistical hypothesis tests and all statistical estimators are derived via statistical models.

  4. Linear probability model - Wikipedia

    en.wikipedia.org/wiki/Linear_probability_model

    For the "linear probability model", this relationship is a particularly simple one, and allows the model to be fitted by linear regression. The model assumes that, for a binary outcome ( Bernoulli trial ), Y {\displaystyle Y} , and its associated vector of explanatory variables, X {\displaystyle X} , [ 1 ]

  5. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete (e.g. a coin toss, a roll of a die) and the probabilities are encoded by a discrete list of the probabilities of the outcomes; in this case the discrete probability distribution is known as probability mass function.

  6. Probability axioms - Wikipedia

    en.wikipedia.org/wiki/Probability_axioms

    The standard probability axioms are the foundations of probability theory introduced by Russian mathematician Andrey Kolmogorov in 1933. [1] These axioms remain central and have direct contributions to mathematics, the physical sciences, and real-world probability cases. [2] There are several other (equivalent) approaches to formalising ...

  7. Graphical model - Wikipedia

    en.wikipedia.org/wiki/Graphical_model

    A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

  8. Probabilistic classification - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_classification

    Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics , probabilistic classification in general is called discrete choice . Some classification models, such as naive Bayes , logistic regression and multilayer perceptrons (when trained under an appropriate loss function ) are naturally ...

  9. Parametric model - Wikipedia

    en.wikipedia.org/wiki/Parametric_model

    In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric model is a family of probability distributions that has a finite number of parameters.

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