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  2. Probability axioms - Wikipedia

    en.wikipedia.org/wiki/Probability_axioms

    Probability theory. 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]

  3. Convolution of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Convolution_of_probability...

    The convolution/sum of probability distributions arises in probability theory and statistics as the operation in terms of probability distributions that corresponds to the addition of independent random variables and, by extension, to forming linear combinations of random variables. The operation here is a special case of convolution in the ...

  4. 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. Typically these axioms formalise probability in terms of a ...

  5. Addition theorem - Wikipedia

    en.wikipedia.org/wiki/Addition_theorem

    Addition theorem. In mathematics, an addition theorem is a formula such as that for the exponential function: that expresses, for a particular function f, f (x + y) in terms of f (x) and f (y). Slightly more generally, as is the case with the trigonometric functions sin and cos, several functions may be involved; this is more apparent than real ...

  6. Law of total probability - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_probability

    The law of total probability is [1] a theorem that states, in its discrete case, if is a finite or countably infinite set of mutually exclusive and collectively exhaustive events, then for any event. or, alternatively, [1] where, for any , if , then these terms are simply omitted from the summation since is finite.

  7. Convergence of random variables - Wikipedia

    en.wikipedia.org/wiki/Convergence_of_random...

    As an example one may consider random variables with densities f n (x) = (1 + cos(2πnx))1 (0,1). These random variables converge in distribution to a uniform U(0, 1), whereas their densities do not converge at all. [3] However, according to Scheffé’s theorem, convergence of the probability density functions implies convergence in ...

  8. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    Unlike a probability, a probability density function can take on values greater than one; for example, the continuous uniform distribution on the interval [0, 1/2] has probability density f(x) = 2 for 0 ≤ x ≤ 1/2 and f(x) = 0 elsewhere. The standard normal distribution has probability density. If a random variable X is given and its ...

  9. Wald's equation - Wikipedia

    en.wikipedia.org/wiki/Wald's_equation

    Wald's equation. In probability theory, Wald's equation, Wald's identity[1] or Wald's lemma[2] is an important identity that simplifies the calculation of the expected value of the sum of a random number of random quantities. In its simplest form, it relates the expectation of a sum of randomly many finite-mean, independent and identically ...

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