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A probability distribution is a special case of the more general notion of a probability measure, which is a function that assigns probabilities satisfying the Kolmogorov axioms to the measurable sets of a measurable space.

Every random variable gives rise to a probability distribution, and this distribution contains most of the important information about the variable. If X is a random variable, the corresponding probability distribution assigns to the interval [a, b] the probability Pr[a ≤ X ≤ b], i.e. the probability that the variable X will take a value in the interval [a, b].

The probability distribution of the variable X can be uniquely described by its cumulative distribution function F(x), which is defined by

for any x in R.

A distribution is called discrete if its cumulative distribution function consists of a sequence of finite jumps, which means that it belongs to a discrete random variable X: a variable which can only attain values from a certain finite or countable set. A distribution is called continuous if its cumulative distribution function is continuous, which means that it belongs to a random variable X for which Pr[ X = x ] = 0 for all x in R.

The so-called absolutely continuous distributions can be expressed by a probability density function: a non-negative Lebesgue integrableIn mathematics, the integral is a generalization of the concept of area from regular figures to regions bounded by functions. Lebesgue integration is a framework for extending the integral to a very large class of functions. The Lebesgue integral plays an function f defined on the reals such that

for all a and b. That discrete distributions do not admit such a density is unsurprising, but there are continuous distributions like the devil's staircaseIn mathematics, a devil's staircase is any function f(x defined on the interval [a,b] that has the following properties: f(x is continuous on [a,b]. there exists a set N of measure 0 such that for all x outside of N the derivative f ′(x exists and i that also do not admit a density.

The support of a distribution is the smallest closed set whose complement has probability zero.

1 List of important probability distributions

Several probability distributions are so important in theory or applications that they have been given specific names:

• Discrete distributions
  • With finite support
    • The degenerate distributionIn mathematics, a degenerate distribution is the probability distribution of a random variable which always has the same value. Examples are a two-headed coin, a die that always comes up six. This doesn't sound very random, but it satisfies the definition at x₀, where X is certain to take the value x₀. This does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in the same formalism.
    • The discrete uniform distributionIn mathematics, the uniform distributions are simple probability distributions. The distribution can be either discrete or continuous. In the discrete case, they can be characterized by saying that all possible values are equally probable. In the continuo, where all elements of a finite setSet theory is the mathematical theory of sets, which represent collections of abstract objects. It has a central role in modern mathematical theory, providing the basic language in which most of mathematics is expressed. For more information on set theory are equally likely. This is supposed to be the distribution of a balanced coin, an unbiased die, a casino roulette or a well-shuffled deck. Also, one can use measurements of quantum states to generate uniform random variables. All these are "physical" or "mechanical" devices, subject to design flaws or perturbations, so the uniform distribution is only an approximation of their behaviour. In digital computers, pseudo-random number generatorsA Pseudorandom number sequence is a sequence of numbers that has been computed by some defined arithmetic process but is effectively a random number sequence for the purpose for which it is required. Although a pseudorandom number sequence in this sense o are used to produced a statistically randomIn ordinary language, the word random is used to express apparent lack of purpose or cause. This suggests that no matter what the cause of something, its nature is not only unknown but the consequences of its operation are also unknown. In most technical discrete uniform distribution.
    • The Bernoulli distribution, which takes value 1 with probability p and value 0 with probability q = 1 − p.
    • The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments.
    • The hypergeometric distribution, which describes the number of successes in the first m of a series of n independent Yes/No experiments, if the total number of successes is known.
  • With infinite support
    • The geometric distribution, a discrete distribution which describes the number of attempts needed to get the first success in a series of independent Yes/No experiments.
    • The negative binomial distribution, a generalization of the geometric distribution to the nth success.
    • The Poisson distribution, which describes a very large number of individually unlikely events that happen in a certain time interval.
      • The Skellam distribution, the distribution of the difference between two independent Poisson-distributed random variables.
    • The Boltzmann distribution, a discrete distribution important in statistical physics which describes the probabilities of the various discrete energy levels of a system in thermal equilibrium. It has a contiuous analogue. Special cases include
      • The Gibbs distribution
      • The Maxwell-Boltzmann distribution
      • The Bose-Einstein distribution
      • The Fermi-Dirac distribution
    • The zeta distribution has uses in applied statistics and statistical mechanics, and perhaps may be of interest to number theorists.
• Continuous distributions
  • Supported on a bounded interval
    • The uniform distribution on [a,b], where all points in a finite interval are equally likely.
    • The Beta distribution on [0,1], of which the uniform distribution is a special case, and which is useful in estimating success probabilities.
    • The triangular distribution on [a, b], a special case of which is the distribution of the sum of two uniformly distributed random variables (the convolution of two uniform distributions).
    • The Wigner semicircle distribution is important in the theory of random matrices .
  • Supported on semi-infinite intervals, usually [0,∞)
    • The exponential distribution, which describes the time between consecutive rare random events in a process with no memory.
    • The Gamma distribution, which describes the time until n consecutive rare random events occur in a process with no memory.
    • The Erlang distribution, which is a special case of the gamma distribution with integral shape parameter, developed to predict waiting times in queuing systems .
    • The log-normal distribution, describing variables which can be modelled as the product of many small independent positive variables.
    • The Weibull distribution, of which the exponential distribution is a special case, is used to model the lifetime of technical devices.
    • The chi-square distribution, which is the sum of the squares of n independent Gaussian random variables. It is a special case of the Gamma distribution, and it is used in goodness-of-fit tests in statistics.
    • The F-distribution, which is the distribution of the ratio of two normally distributed random variables, used in the analysis of variance.
  • Supported on the whole real line
    • The normal distribution, also called the Gaussian or the bell curve. It is ubiquitous in nature and statistics due to the central limit theorem: every variable that can be modelled as a sum of many small independent variables is approximately normal.
    • The Student's t-distribution, useful for estimating unknown means of Gaussian populations.
    • The Cauchy distribution, an example of a distribution which does not have an expected value or a variance. In physics it is usually called a Lorentzian profile, and is associated with many processes, including resonance energy distribution, impact and natural spectral line broadening and quadratic stark line broadening.
    • The Voigt distribution or Voigt profile is the convolution of a normal distribution and a Cauchy distribution. It is found in spectroscopy when spectral line profiles are broadend by a mixture of Lorentzian and Doppler broadening mechanisms.
• Joint distributions
  • Two or more random variables on the same sample space
    • Bivariate distribution
    • Conditional distribution
    • Multivariate distribution
    • Multinomial distribution, a generalization of the binomial distribution.
• Matrix-valued distributions
  • Wishart distribution
  • Matrix normal distribution
  • Matrix t distribution