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  2. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...

  3. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    Mode: for a discrete random variable, the value with highest probability; for an absolutely continuous random variable, a location at which the probability density function has a local peak. Quantile : the q-quantile is the value x {\displaystyle x} such that P ( X < x ) = q {\displaystyle P(X<x)=q} .

  4. Random variable - Wikipedia

    en.wikipedia.org/wiki/Random_variable

    When the image (or range) of is finitely or infinitely countable, the random variable is called a discrete random variable [5]: 399 and its distribution is a discrete probability distribution, i.e. can be described by a probability mass function that assigns a probability to each value in the image of .

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    In probability theory, the Fourier transform of the probability distribution of a real-valued random variable is closely connected to the characteristic function of that variable, which is defined as the expected value of , as a function of the real variable (the frequency parameter of the Fourier transform).

  6. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    Now consider a random variable X which has a probability density function given by a function f on the real number line. This means that the probability of X taking on a value in any given open interval is given by the integral of f over that interval.

  7. Variance - Wikipedia

    en.wikipedia.org/wiki/Variance

    Being a function of random variables, the sample variance is itself a random variable, and it is natural to study its distribution. In the case that Y i are independent observations from a normal distribution , Cochran's theorem shows that the unbiased sample variance S 2 follows a scaled chi-squared distribution (see also: asymptotic ...

  8. Notation in probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Notation_in_probability...

    The probability is sometimes written to distinguish it from other functions and measure P to avoid having to define "P is a probability" and () is short for ({: ()}), where is the event space, is a random variable that is a function of (i.e., it depends upon ), and is some outcome of interest within the domain specified by (say, a particular ...

  9. Distribution of the product of two random variables - Wikipedia

    en.wikipedia.org/wiki/Distribution_of_the...

    A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product = is a product distribution.