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  2. Probability measure - Wikipedia

    en.wikipedia.org/wiki/Probability_measure

    Intuitively, the additivity property says that the probability assigned to the union of two disjoint (mutually exclusive) events by the measure should be the sum of the probabilities of the events; for example, the value assigned to the outcome "1 or 2" in a throw of a dice should be the sum of the values assigned to the outcomes "1" and "2 ...

  3. Borel–Kolmogorov paradox - Wikipedia

    en.wikipedia.org/wiki/Borel–Kolmogorov_paradox

    In case (1) above, the conditional probability that the longitude λ lies in a set E given that φ = 0 can be written P(λ ∈ E | φ = 0). Elementary probability theory suggests this can be computed as P(λ ∈ E and φ = 0)/P(φ = 0), but that expression is not well-defined since P(φ = 0) = 0.

  4. Chebyshev's inequality - Wikipedia

    en.wikipedia.org/wiki/Chebyshev's_inequality

    2.2 Measure-theoretic statement. 3 Example. ... Download as PDF; Printable version ... The one-sided variant can be used to prove the proposition that for probability ...

  5. 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 ...

  6. Measure (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Measure_(mathematics)

    Every probability space gives rise to a measure which takes the value 1 on the whole space (and therefore takes all its values in the unit interval [0, 1]). Such a measure is called a probability measure or distribution. See the list of probability distributions for instances.

  7. Law of the unconscious statistician - Wikipedia

    en.wikipedia.org/wiki/Law_of_the_unconscious...

    In fact, the discrete case (although without the restriction to probability measures) is the first step in proving the general measure-theoretic formulation, as the general version follows therefrom by an application of the monotone convergence theorem. [7] Without any major changes, the result can also be formulated in the setting of outer ...

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