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  2. Template:Probability fundamentals - Wikipedia

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

    Download as PDF; Printable version; In other projects ... Probability measure; Random variable. ... Template documentation

  3. Borel–Kolmogorov paradox - Wikipedia

    en.wikipedia.org/wiki/Borel–Kolmogorov_paradox

    Measure theory provides a way to define a conditional probability, using the limit of events R ab = {φ : a < φ < b} which are horizontal rings (curved surface zones of spherical segments) consisting of all points with latitude between a and b.

  4. Probability theory - Wikipedia

    en.wikipedia.org/wiki/Probability_theory

    If is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on for any CDF, and vice versa. The measure corresponding to a CDF is said to be induced by the CDF. This measure coincides with the pmf for discrete variables and PDF for continuous variables, making the measure-theoretic approach free of fallacies.

  5. Probability measure - Wikipedia

    en.wikipedia.org/wiki/Probability_measure

    In mathematics, a probability measure is a real-valued function defined on a set of events in a σ-algebra that satisfies measure properties such as countable additivity. [1] The difference between a probability measure and the more general notion of measure (which includes concepts like area or volume ) is that a probability measure must ...

  6. Unit measure - Wikipedia

    en.wikipedia.org/wiki/Unit_measure

    The term measure here refers to the measure-theoretic approach to probability. Violations of unit measure have been reported in arguments about the outcomes of events [2] [3] under which events acquire "probabilities" that are not the probabilities of probability theory. In situations such as these the term "probability" serves as a false ...

  7. Distribution function (measure theory) - Wikipedia

    en.wikipedia.org/wiki/Distribution_function...

    When the underlying measure on (, ()) is finite, the distribution function in Definition 3 differs slightly from the standard definition of the distribution function (in the sense of probability theory) as given by Definition 2 in that for the former, = while for the latter, () = = ().

  8. Kolmogorov extension theorem - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov_extension_theorem

    The measure-theoretic approach to stochastic processes starts with a probability space and defines a stochastic process as a family of functions on this probability space. However, in many applications the starting point is really the finite-dimensional distributions of the stochastic process.

  9. Computable number - Wikipedia

    en.wikipedia.org/wiki/Computable_number

    Print/export Download as PDF; Printable version; In other projects ... from a computability theoretic or measure theoretic perspective, ...

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