Probability distribution

Mathematical function for the probability a given outcome occurs in an experiment

Part of a series on statistics

Probability theory
File:Standard deviation diagram micro.svg

* Probability
* Axioms
* Determinism
* System
* Indeterminism
* Randomness

* Probability space
* Sample space
* Event
* Collectively exhaustive events
* Elementary event
* Mutual exclusivity
* Outcome
* Singleton
* Experiment
* Bernoulli trial
* Probability distribution
* Bernoulli distribution
* Binomial distribution
* Normal distribution
* Probability measure
* Random variable
* Bernoulli process
* Continuous or discrete
* Expected value
* Markov chain
* Observed value
* Random walk
* Stochastic process

* Complementary event
* Joint probability
* Marginal probability
* Conditional probability

* Independence
* Conditional independence
* Law of total probability
* Law of large numbers
* Bayes' theorem
* Boole's inequality

* Venn diagram
* Tree diagram

* v
* t
* e

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.[1][2] It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space).[3]

For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.5 for X = tails (assuming that the coin is fair). Examples of random phenomena include the weather conditions at some future date, the height of a randomly selected person, the fraction of male students in a school, the results of a survey to be conducted, etc.[4]

## Contents

* 1 Introduction
* 2 General probability definition
* 3 Terminology
* 3.1 Basic terms
* 3.2 Discrete probability distributions
* 3.3 Absolutely continuous probability distributions
* 3.4 Related terms
* 4 Cumulative distribution function
* 5 Discrete probability distribution
* 5.1 Cumulative distribution function
* 5.2 Dirac delta representation
* 5.3 Indicator-function representation
* 5.4 One-point distribution
* 6 Absolutely continuous probability distribution
* 6.1 Cumulative distribution function
* 7 Kolmogorov definition
* 8 Other kinds of distributions
* 9 Random number generation
* 10 Common probability distributions and their applications
* 10.1 Linear growth (e.g. errors, offsets)
* 10.2 Exponential growth (e.g. prices, incomes, populations)
* 10.3 Uniformly distributed quantities
* 10.4 Bernoulli trials (yes/no events, with a given probability)
* 10.5 Categorical outcomes (events with K possible outcomes)
* 10.6 Poisson process (events that occur independently with a given rate)
* 10.7 Absolute values of vectors with normally distributed components
* 10.8 Normally distributed quantities operated with sum of squares
* 10.9 As conjugate prior distributions in Bayesian inference
* 10.10 Some specialized applications of probability distributions
* 11 See also
* 11.1 Lists
* 12 References
* 12.1 Citations
* 12.2 Sources
* 13 External links

## Introduction[edit | edit source]

File:Dice Distribution (bar).svg

The probability mass function (pmf) Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(S)} specifies the probability distribution for the sum  S {\displaystyle S} of counts from two dice. For example, the figure shows that Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle p(11)=2/36=1/18} . The pmf allows the computation of probabilities of events such as Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle P(X>9)=1/12+1/18+1/36=1/6} , and all other probabilities in the distribution.

A probability distribution is a mathematical description of the probabilities of events, subsets of the sample space. The sample space, often denoted by  Ω {\displaystyle \Omega } , is the set of all possible outcomes of a random phenomenon being observed; it may be any set: a set of real numbers, a set of vectors, a set of arbitrary non-numerical values, etc. For example, the sample space of a coin flip would be Ω = {heads, tails}.

To define probability distributions for the specific case of random variables (so the sample space can be seen as a numeric set), it is common to distinguish between discrete and absolutely continuous random variables. In the discrete case, it is sufficient to specify a probability mass function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p} assigning a probability to each possible outcome: for example, when throwing a fair dice, each of the six values 1 to 6 has the probability 1/6. The probability of an event is then defined to be the sum of the probabilities of the outcomes that satisfy the event; for example, the probability of the event "the die rolls an even value" is Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(2) + p(4) + p(6) = 1/6 + 1/6 + 1/6 = 1/2.}

In contrast, when a random variable takes values from a continuum then typically, any individual outcome has probability zero and only events that include infinitely many outcomes, such as intervals, can have positive probability. For example, consider measuring the weight of a piece of ham in the supermarket, and assume the scale has many digits of precision. The probability that it weighs exactly 500 g is zero, as it will most likely have some non-zero decimal digits. Nevertheless, one might demand, in quality control, that a package of "500 g" of ham must weigh between 490 g and 510 g with at least 98% probability, and this demand is less sensitive to the accuracy of measurement instruments.

File:Combined Cumulative Distribution Graphs.png

The left shows the probability density function. The right shows the cumulative distribution function, for which the value at a equals the area under the probability density curve to the left of a.

Absolutely continuous probability distributions can be described in several ways. The probability density function describes the infinitesimal probability of any given value, and the probability that the outcome lies in a given interval can be computed by integrating the probability density function over that interval.[5] An alternative description of the distribution is by means of the cumulative distribution function, which describes the probability that the random variable is no larger than a given value (i.e., Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X < x)} for some Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} ). The cumulative distribution function is the area under the probability density function from Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle -\infty} to Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} , as described by the picture to the right.[6]

## General probability definition[edit | edit source]

A probability distribution can be described in various forms, such as by a probability mass function or a cumulative distribution function. One of the most general descriptions, which applies for absolutely continuous and discrete variables, is by means of a probability function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P\colon \mathcal{A} \to \Reals} whose input space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathcal{A}} is related[clarification needed] to the sample space, and gives a real number probability as its output.[citation needed]

The probability function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P} can take as argument subsets of the sample space itself, as in the coin toss example, where the function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P} was defined so that P(heads) = 0.5 and P(tails) = 0.5. However, because of the widespread use of random variables, which transform the sample space into a set of numbers (e.g., Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \R} , Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \N} ), it is more common to study probability distributions whose argument are subsets of these particular kinds of sets (number sets),[7] and all probability distributions discussed in this article are of this type. It is common to denote as Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E)} the probability that a certain value of the variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} belongs to a certain event Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E} .[4][8]

The above probability function only characterizes a probability distribution if it satisfies all the Kolmogorov axioms, that is:

1. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E) \ge 0 \; \forall E \in \mathcal{A}} , so the probability is non-negative
2. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E) \le 1 \; \forall E \in \mathcal{A}} , so no probability exceeds Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1}
3. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in \bigsqcup_{i} E_i ) = \sum_i P(X \in E_i)} for any disjoint family of sets Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \\{ E_i \\}}

The concept of probability function is made more rigorous by defining it as the element of a probability space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (X, \mathcal{A}, P)} , where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} is the set of possible outcomes, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathcal{A}} is the set of all subsets Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E \subset X} whose probability can be measured, and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P} is the probability function, or probability measure, that assigns a probability to each of these measurable subsets Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E \in \mathcal{A}} .[9]

Probability distributions usually belong to one of two classes. A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete (e.g. a coin toss, a roll of a die) and the probabilities are encoded by a discrete list of the probabilities of the outcomes; in this case the discrete probability distribution is known as probability mass function. On the other hand, absolutely continuous probability distributions are applicable to scenarios where the set of possible outcomes can take on values in a continuous range (e.g. real numbers), such as the temperature on a given day. In the absolutely continuous case, probabilities are described by a probability density function, and the probability distribution is by definition the integral of the probability density function.[4][5][8] The normal distribution is a commonly encountered absolutely continuous probability distribution. More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more general probability measures.

A probability distribution whose sample space is one-dimensional (for example real numbers, list of labels, ordered labels or binary) is called univariate, while a distribution whose sample space is a vector space of dimension 2 or more is called multivariate. A univariate distribution gives the probabilities of a single random variable taking on various different values; a multivariate distribution (a joint probability distribution) gives the probabilities of a random vector – a list of two or more random variables – taking on various combinations of values. Important and commonly encountered univariate probability distributions include the binomial distribution, the hypergeometric distribution, and the normal distribution. A commonly encountered multivariate distribution is the multivariate normal distribution.

Besides the probability function, the cumulative distribution function, the probability mass function and the probability density function, the moment generating function and the characteristic function also serve to identify a probability distribution, as they uniquely determine an underlying cumulative distribution function.[10]

File:Standard deviation diagram.svg

The probability density function (pdf) of the normal distribution, also called Gaussian or "bell curve", the most important absolutely continuous random distribution. As notated on the figure, the probabilities of intervals of values correspond to the area under the curve.

## Terminology[edit | edit source]

Some key concepts and terms, widely used in the literature on the topic of probability distributions, are listed below.[1]

### Basic terms[edit | edit source]

* Random variable: takes values from a sample space; probabilities describe which values and set of values are taken more likely.
* Event: set of possible values (outcomes) of a random variable that occurs with a certain probability.
* Probability function or probability measure: describes the probability Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E)} that the event Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E,} occurs.[11]
* Cumulative distribution function: function evaluating the probability that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} will take a value less than or equal to Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} for a random variable (only for real-valued random variables).
* Quantile function: the inverse of the cumulative distribution function. Gives Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} such that, with probability Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle q} , Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} will not exceed Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} .

### Discrete probability distributions[edit | edit source]

* Discrete probability distribution: for many random variables with finitely or countably infinitely many values.
* Probability mass function (pmf): function that gives the probability that a discrete random variable is equal to some value.
* Frequency distribution: a table that displays the frequency of various outcomes in a sample.
* Relative frequency distribution: a frequency distribution where each value has been divided (normalized) by a number of outcomes in a sample (i.e. sample size).
* Categorical distribution: for discrete random variables with a finite set of values.

### Absolutely continuous probability distributions[edit | edit source]

* Absolutely continuous probability distribution: for many random variables with uncountably many values.
* Probability density function (pdf) or Probability density: 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 random variable would equal that sample.

### Related terms[edit | edit source]

* Support: set of values that can be assumed with non-zero probability by the random variable. For a random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} , it is sometimes denoted as Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle R_X} .
* Tail:[12] the regions close to the bounds of the random variable, if the pmf or pdf are relatively low therein. Usually has the form Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X > a} , Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X < b} or a union thereof.
* Head:[12] the region where the pmf or pdf is relatively high. Usually has the form Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a < X < b} .
* Expected value or mean: the weighted average of the possible values, using their probabilities as their weights; or the continuous analog thereof.
* Median: the value such that the set of values less than the median, and the set greater than the median, each have probabilities no greater than one-half.
* 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 Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} such that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X < x) = q} .
* Variance: the second moment of the pmf or pdf about the mean; an important measure of the dispersion of the distribution.
* Standard deviation: the square root of the variance, and hence another measure of dispersion.
* Symmetry: a property of some distributions in which the portion of the distribution to the left of a specific value (usually the median) is a mirror image of the portion to its right.
* Skewness: a measure of the extent to which a pmf or pdf "leans" to one side of its mean. The third standardized moment of the distribution.
* Kurtosis: a measure of the "fatness" of the tails of a pmf or pdf. The fourth standardized moment of the distribution.

## Cumulative distribution function[edit | edit source]

In the special case of a real-valued random variable, the probability distribution can equivalently be represented by a cumulative distribution function instead of a probability measure. The cumulative distribution function of a random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} with regard to a probability distribution Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p} is defined as Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x) = P(X \leq x).}

The cumulative distribution function of any real-valued random variable has the properties:

*   * Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x)} is non-decreasing;
*   * Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x)} is right-continuous;
*   * Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 0 \le F(x) \le 1} ;
*   * Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{x \to -\infty} F(x) = 0} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{x \to \infty} F(x) = 1} ; and
*   * Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Pr(a < X \le b) = F(b) - F(a)} .

Conversely, any function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F:\mathbb{R}\to\mathbb{R}} that satisfies the first four of the properties above is the cumulative distribution function of some probability distribution on the real numbers.[13]

Any probability distribution can be decomposed as the sum of a discrete, an absolutely continuous and a singular continuous distribution,[14] and thus any cumulative distribution function admits a decomposition as the sum of the three according cumulative distribution functions.

## Discrete probability distribution[edit | edit source]

Main article: Probability mass function

File:Discrete probability distrib.svg

The probability mass function of a discrete probability distribution. The probabilities of the singletons {1}, {3}, and {7} are respectively 0.2, 0.5, 0.3. A set not containing any of these points has probability zero.

File:Discrete probability distribution.svg

The cdf of a discrete probability distribution, ...

File:Normal probability distribution.svg

... of a continuous probability distribution, ...

File:Mixed probability distribution.svg

... of a distribution which has both a continuous part and a discrete part.

A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values[15] (almost surely)[16] which means that the probability of any event Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E} can be expressed as a (finite or countably infinite) sum: Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X\in E) = \sum_{\omega\in A} P(X = \omega),} where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A} is a countable set. Thus the discrete random variables are exactly those with a probability mass function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(x) = P(X=x)} . In the case where the range of values is countably infinite, these values have to decline to zero fast enough for the probabilities to add up to 1. For example, if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(n) = \tfrac{1}{2^n}} for Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n = 1, 2, ...} , the sum of probabilities would be Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1/2 + 1/4 + 1/8 + \dots = 1} .

A discrete random variable is a random variable whose probability distribution is discrete.

Well-known discrete probability distributions used in statistical modeling include the Poisson distribution, the Bernoulli distribution, the binomial distribution, the geometric distribution, the negative binomial distribution and categorical distribution.[3] When a sample (a set of observations) is drawn from a larger population, the sample points have an empirical distribution that is discrete, and which provides information about the population distribution. Additionally, the discrete uniform distribution is commonly used in computer programs that make equal-probability random selections between a number of choices.

### Cumulative distribution function[edit | edit source]

A real-valued discrete random variable can equivalently be defined as a random variable whose cumulative distribution function increases only by jump discontinuities—that is, its cdf increases only where it "jumps" to a higher value, and is constant in intervals without jumps. The points where jumps occur are precisely the values which the random variable may take. Thus the cumulative distribution function has the form Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x) = P(X \leq x) = \sum_{\omega \leq x} p(\omega).}

Note that the points where the cdf jumps always form a countable set; this may be any countable set and thus may even be dense in the real numbers.

### Dirac delta representation[edit | edit source]

A discrete probability distribution is often represented with Dirac measures, the probability distributions of deterministic random variables. For any outcome Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \omega} , let Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_\omega} be the Dirac measure concentrated at Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \omega} . Given a discrete probability distribution, there is a countable set Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A} with Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in A) = 1} and a probability mass function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p} . If Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E} is any event, then Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E) = \sum_{\omega \in A} p(\omega) \delta_\omega(E),} or in short, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P_X = \sum_{\omega \in A} p(\omega) \delta_\omega.}

Similarly, discrete distributions can be represented with the Dirac delta function as a generalized probability density function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} , where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(x) = \sum_{\omega \in A} p(\omega) \delta(x - \omega),} which means Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in E) = \int_E f(x) \, dx = \sum_{\omega \in A} p(\omega) \int_E \delta(x - \omega) = \sum_{\omega \in A \cap E} p(\omega)} for any event Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E.} [17]

### Indicator-function representation[edit | edit source]

For a discrete random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} , let Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle u_0, u_1, \dots} be the values it can take with non-zero probability. Denote

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Omega_i=X^{-1}(u_i)= \\{\omega: X(\omega)=u_i\\},\, i=0, 1, 2, \dots}

These are disjoint sets, and for such sets

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P\left(\bigcup_i \Omega_i\right)=\sum_i P(\Omega_i)=\sum_i P(X=u_i)=1.}

It follows that the probability that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} takes any value except for Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle u_0, u_1, \dots} is zero, and thus one can write Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X(\omega)=\sum_i u_i 1_{\Omega_i}(\omega)}

except on a set of probability zero, where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1_A} is the indicator function of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A} . This may serve as an alternative definition of discrete random variables.

### One-point distribution[edit | edit source]

A special case is the discrete distribution of a random variable that can take on only one fixed value; in other words, it is a deterministic distribution. Expressed formally, the random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} has a one-point distribution if it has a possible outcome Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x} such that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X{=}x)=1.} [18] All other possible outcomes then have probability 0. Its cumulative distribution function jumps immediately from 0 to 1.

## Absolutely continuous probability distribution[edit | edit source]

Main article: Probability density function

An absolutely continuous probability distribution is a probability distribution on the real numbers with uncountably many possible values, such as a whole interval in the real line, and where the probability of any event can be expressed as an integral.[19] More precisely, a real random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} has an absolutely continuous probability distribution if there is a function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f: \Reals \to [0, \infty]} such that for each interval Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle [a,b] \subset \mathbb{R}} the probability of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} belonging to Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle [a,b]} is given by the integral of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} over Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I} :[20][21] Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P\left(a \le X \le b \right) = \int_a^b f(x) \, dx .} This is the definition of a probability density function, so that absolutely continuous probability distributions are exactly those with a probability density function. In particular, the probability for Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} to take any single value Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a} (that is, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a \le X \le a} ) is zero, because an integral with coinciding upper and lower limits is always equal to zero. If the interval Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle [a,b]} is replaced by any measurable set Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A} , the according equality still holds: Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(X \in A) = \int_A f(x) \, dx .}

An absolutely continuous random variable is a random variable whose probability distribution is absolutely continuous.

There are many examples of absolutely continuous probability distributions: normal, uniform, chi-squared, and others.

### Cumulative distribution function[edit | edit source]

Absolutely continuous probability distributions as defined above are precisely those with an absolutely continuous cumulative distribution function. In this case, the cumulative distribution function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F} has the form Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x) = P(X \leq x) = \int_{-\infty}^x f(t)\,dt} where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} is a density of the random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} with regard to the distribution Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P} .

Note on terminology: Absolutely continuous distributions ought to be distinguished from continuous distributions, which are those having a continuous cumulative distribution function. Every absolutely continuous distribution is a continuous distribution but the converse is not true, there exist singular distributions, which are neither absolutely continuous nor discrete nor a mixture of those, and do not have a density. An example is given by the Cantor distribution. Some authors however use the term "continuous distribution" to denote all distributions whose cumulative distribution function is absolutely continuous, i.e. refer to absolutely continuous distributions as continuous distributions.[4]

For a more general definition of density functions and the equivalent absolutely continuous measures see absolutely continuous measure.

## Kolmogorov definition[edit | edit source]

Main articles: Probability space and Probability measure

In the measure-theoretic formalization of probability theory, a random variable is defined as a measurable function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} from a probability space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (\Omega, \mathcal{F}, \mathbb{P})} to a measurable space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (\mathcal{X},\mathcal{A})} . Given that probabilities of events of the form Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \\{\omega\in\Omega\mid X(\omega)\in A\\}} satisfy Kolmogorov's probability axioms, the probability distribution of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} is the image measure Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X_*\mathbb{P}} of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} , which is a probability measure on Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (\mathcal{X},\mathcal{A})} satisfying Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X_*\mathbb{P} = \mathbb{P}X^{-1}} .[22][23][24]

## Other kinds of distributions[edit | edit source]

File:Rabinovich Fabrikant 2314.png

One solution for the Rabinovich–Fabrikant equations. What is the probability of observing a state on a certain place of the support (i.e., the red subset)?

Absolutely continuous and discrete distributions with support on Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{R}^k} or Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{N}^k} are extremely useful to model a myriad of phenomena,[4][6] since most practical distributions are supported on relatively simple subsets, such as hypercubes or balls. However, this is not always the case, and there exist phenomena with supports that are actually complicated curves Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \gamma: [a, b] \rightarrow \mathbb{R}^n} within some space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{R}^n} or similar. In these cases, the probability distribution is supported on the image of such curve, and is likely to be determined empirically, rather than finding a closed formula for it.[25]

One example is shown in the figure to the right, which displays the evolution of a system of differential equations (commonly known as the Rabinovich–Fabrikant equations) that can be used to model the behaviour of Langmuir waves in plasma.[26] When this phenomenon is studied, the observed states from the subset are as indicated in red. So one could ask what is the probability of observing a state in a certain position of the red subset; if such a probability exists, it is called the probability measure of the system.[27][25]

This kind of complicated support appears quite frequently in dynamical systems. It is not simple to establish that the system has a probability measure, and the main problem is the following. Let Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t_1 \ll t_2 \ll t_3} be instants in time and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle O} a subset of the support; if the probability measure exists for the system, one would expect the frequency of observing states inside set Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle O} would be equal in interval Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle [t_1,t_2]} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle [t_2,t_3]} , which might not happen; for example, it could oscillate similar to a sine, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \sin(t)} , whose limit when Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t \rightarrow \infty} does not converge. Formally, the measure exists only if the limit of the relative frequency converges when the system is observed into the infinite future.[28] The branch of dynamical systems that studies the existence of a probability measure is ergodic theory.

Note that even in these cases, the probability distribution, if it exists, might still be termed "absolutely continuous" or "discrete" depending on whether the support is uncountable or countable, respectively.

## Random number generation[edit | edit source]

Main article: Pseudo-random number sampling

Most algorithms are based on a pseudorandom number generator that produces numbers Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} that are uniformly distributed in the half-open interval [0, 1). These random variates Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} are then transformed via some algorithm to create a new random variate having the required probability distribution. With this source of uniform pseudo-randomness, realizations of any random variable can be generated.[29]

For example, suppose Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} has a uniform distribution between 0 and 1. To construct a random Bernoulli variable for some Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 0 < p < 1} , we define Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X = \begin{cases} 1,& \text{if } U<p\\\ 0,& \text{if } U\geq p \end{cases}} so that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Pr(X=1) = \Pr(U<p) = p, \quad \Pr(X=0) = \Pr(U\geq p) = 1-p.}

This random variable X has a Bernoulli distribution with parameter Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p} .[29] Note that this is a transformation of discrete random variable.

For a distribution function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F} of an absolutely continuous random variable, an absolutely continuous random variable must be constructed. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F^{\mathit{inv}}} , an inverse function of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F} , relates to the uniform variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} : Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle {U\leq F(x)} = {F^{\mathit{inv}}(U)\leq x}.}

For example, suppose a random variable that has an exponential distribution Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x) = 1 - e^{-\lambda x}} must be constructed.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} F(x) = u &\Leftrightarrow 1-e^{-\lambda x} = u \\\\[2pt] &\Leftrightarrow e^{-\lambda x } = 1-u \\\\[2pt] &\Leftrightarrow -\lambda x = \ln(1-u) \\\\[2pt] &\Leftrightarrow x = \frac{-1}{\lambda}\ln(1-u) \end{align}} so Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F^{\mathit{inv}}(u) = \frac{-1}{\lambda}\ln(1-u)} and if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} has a Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U(0,1)} distribution, then the random variable Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X} is defined by Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X = F^{\mathit{inv}}(U) = \frac{-1}{\lambda} \ln(1-U)} . This has an exponential distribution of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lambda} .[29]

A frequent problem in statistical simulations (the Monte Carlo method) is the generation of pseudo-random numbers that are distributed in a given way.

## Common probability distributions and their applications[edit | edit source]

For a more comprehensive list, see List of probability distributions.

The concept of the probability distribution and the random variables which they describe underlies the mathematical discipline of probability theory, and the science of statistics. There is spread or variability in almost any value that can be measured in a population (e.g. height of people, durability of a metal, sales growth, traffic flow, etc.); almost all measurements are made with some intrinsic error; in physics, many processes are described probabilistically, from the kinetic properties of gases to the quantum mechanical description of fundamental particles. For these and many other reasons, simple numbers are often inadequate for describing a quantity, while probability distributions are often more appropriate.

The following is a list of some of the most common probability distributions, grouped by the type of process that they are related to. For a more complete list, see list of probability distributions, which groups by the nature of the outcome being considered (discrete, absolutely continuous, multivariate, etc.)

All of the univariate distributions below are singly peaked; that is, it is assumed that the values cluster around a single point. In practice, actually observed quantities may cluster around multiple values. Such quantities can be modeled using a mixture distribution.

### Linear growth (e.g. errors, offsets)[edit | edit source]

* Normal distribution (Gaussian distribution), for a single such quantity; the most commonly used absolutely continuous distribution

### Exponential growth (e.g. prices, incomes, populations)[edit | edit source]

* Log-normal distribution, for a single such quantity whose log is normally distributed
* Pareto distribution, for a single such quantity whose log is exponentially distributed; the prototypical power law distribution

### Uniformly distributed quantities[edit | edit source]

* Discrete uniform distribution, for a finite set of values (e.g. the outcome of a fair die)
* Continuous uniform distribution, for absolutely continuously distributed values

### Bernoulli trials (yes/no events, with a given probability)[edit | edit source]

* Basic distributions:
* Bernoulli distribution, for the outcome of a single Bernoulli trial (e.g. success/failure, yes/no)
* Binomial distribution, for the number of "positive occurrences" (e.g. successes, yes votes, etc.) given a fixed total number of independent occurrences
* Negative binomial distribution, for binomial-type observations but where the quantity of interest is the number of failures before a given number of successes occurs
* Geometric distribution, for binomial-type observations but where the quantity of interest is the number of failures before the first success; a special case of the negative binomial distribution
* Related to sampling schemes over a finite population:
* Hypergeometric distribution, for the number of "positive occurrences" (e.g. successes, yes votes, etc.) given a fixed number of total occurrences, using sampling without replacement
* Beta-binomial distribution, for the number of "positive occurrences" (e.g. successes, yes votes, etc.) given a fixed number of total occurrences, sampling using a Pólya urn model (in some sense, the "opposite" of sampling without replacement)

### Categorical outcomes (events with K possible outcomes)[edit | edit source]

* Categorical distribution, for a single categorical outcome (e.g. yes/no/maybe in a survey); a generalization of the Bernoulli distribution
* Multinomial distribution, for the number of each type of categorical outcome, given a fixed number of total outcomes; a generalization of the binomial distribution
* Multivariate hypergeometric distribution, similar to the multinomial distribution, but using sampling without replacement; a generalization of the hypergeometric distribution

### Poisson process (events that occur independently with a given rate)[edit | edit source]

* Poisson distribution, for the number of occurrences of a Poisson-type event in a given period of time
* Exponential distribution, for the time before the next Poisson-type event occurs
* Gamma distribution, for the time before the next k Poisson-type events occur

### Absolute values of vectors with normally distributed components[edit | edit source]

* Rayleigh distribution, for the distribution of vector magnitudes with Gaussian distributed orthogonal components. Rayleigh distributions are found in RF signals with Gaussian real and imaginary components.
* Rice distribution, a generalization of the Rayleigh distributions for where there is a stationary background signal component. Found in Rician fading of radio signals due to multipath propagation and in MR images with noise corruption on non-zero NMR signals.

### Normally distributed quantities operated with sum of squares[edit | edit source]

* Chi-squared distribution, the distribution of a sum of squared standard normal variables; useful e.g. for inference regarding the sample variance of normally distributed samples (see chi-squared test)
* Student's t distribution, the distribution of the ratio of a standard normal variable and the square root of a scaled chi squared variable; useful for inference regarding the mean of normally distributed samples with unknown variance (see Student's t-test)
* F-distribution, the distribution of the ratio of two scaled chi squared variables; useful e.g. for inferences that involve comparing variances or involving R-squared (the squared correlation coefficient)

### As conjugate prior distributions in Bayesian inference[edit | edit source]

Main article: Conjugate prior

* Beta distribution, for a single probability (real number between 0 and 1); conjugate to the Bernoulli distribution and binomial distribution
* Gamma distribution, for a non-negative scaling parameter; conjugate to the rate parameter of a Poisson distribution or exponential distribution, the precision (inverse variance) of a normal distribution, etc.
* Dirichlet distribution, for a vector of probabilities that must sum to 1; conjugate to the categorical distribution and multinomial distribution; generalization of the beta distribution
* Wishart distribution, for a symmetric non-negative definite matrix; conjugate to the inverse of the covariance matrix of a multivariate normal distribution; generalization of the gamma distribution[30]

### Some specialized applications of probability distributions[edit | edit source]

* The cache language models and other statistical language models used in natural language processing to assign probabilities to the occurrence of particular words and word sequences do so by means of probability distributions.
* In quantum mechanics, the probability density of finding the particle at a given point is proportional to the square of the magnitude of the particle's wavefunction at that point (see Born rule). Therefore, the probability distribution function of the position of a particle is described by Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\textstyle P_{a\le x\le b} (t) = \int_a^b d x\,|\Psi(x,t)|^2 } , probability that the particle's position x will be in the interval a ≤ x ≤ b in dimension one, and a similar triple integral in dimension three. This is a key principle of quantum mechanics.[31]
* Probabilistic load flow in power-flow study explains the uncertainties of input variables as probability distribution and provides the power flow calculation also in term of probability distribution.[32]
* Prediction of natural phenomena occurrences based on previous frequency distributions such as tropical cyclones, hail, time in between events, etc.[33]

## See also[edit | edit source]

* Conditional probability distribution
* Joint probability distribution
* Quasiprobability distribution
* Empirical probability distribution
* Histogram
* Riemann–Stieltjes integral application to probability theory

### Lists[edit | edit source]

* List of probability distributions
* List of statistical topics

## References[edit | edit source]

### Citations[edit | edit source]

1. ^ a b Everitt, Brian (2006). The Cambridge dictionary of statistics (3rd ed.). Cambridge, UK: Cambridge University Press. ISBN 978-0-511-24688-3. OCLC 161828328.
2. ^ Ash, Robert B. (2008). Basic probability theory (Dover ed.). Mineola, N.Y.: Dover Publications. pp. 66–69. ISBN 978-0-486-46628-6. OCLC 190785258.
3. ^ a b Evans, Michael; Rosenthal, Jeffrey S. (2010). Probability and statistics: the science of uncertainty (2nd ed.). New York: W.H. Freeman and Co. p. 38\. ISBN 978-1-4292-2462-8. OCLC 473463742.
4. ^ a b c d e Ross, Sheldon M. (2010). A first course in probability. Pearson.
5. ^ a b "1.3.6.1. What is a Probability Distribution". www.itl.nist.gov. Retrieved 2020-09-10.
6. ^ a b A modern introduction to probability and statistics : understanding why and how. Dekking, Michel, 1946-. London: Springer. 2005. ISBN 978-1-85233-896-1. OCLC 262680588.`{{cite book}}`: CS1 maint: others (link)
7. ^ Walpole, R.E.; Myers, R.H.; Myers, S.L.; Ye, K. (1999). Probability and statistics for engineers. Prentice Hall.
8. ^ a b DeGroot, Morris H.; Schervish, Mark J. (2002). Probability and Statistics. Addison-Wesley.
9. ^ Billingsley, P. (1986). Probability and measure. Wiley. ISBN 9780471804789.
10. ^ Shephard, N.G. (1991). "From characteristic function to distribution function: a simple framework for the theory". Econometric Theory. 7 (4): 519–529. doi:10.1017/S0266466600004746. S2CID 14668369.
11. ^ Chapters 1 and 2 of Vapnik (1998)
12. ^ a b More information and examples can be found in the articles Heavy-tailed distribution, Long-tailed distribution, fat-tailed distribution
13. ^ Erhan, Çınlar (2011). Probability and stochastics. New York: Springer. p. 57\. ISBN 9780387878584.
14. ^ see Lebesgue's decomposition theorem
15. ^ Erhan, Çınlar (2011). Probability and stochastics. New York: Springer. p. 51\. ISBN 9780387878591. OCLC 710149819.
16. ^ Cohn, Donald L. (1993). Measure theory. Birkhäuser.
17. ^ Khuri, André I. (March 2004). "Applications of Dirac's delta function in statistics". International Journal of Mathematical Education in Science and Technology. 35 (2): 185–195. doi:10.1080/00207390310001638313. ISSN 0020-739X. S2CID 122501973.
18. ^ Fisz, Marek (1963). Probability Theory and Mathematical Statistics (3rd ed.). John Wiley & Sons. p. 129\. ISBN 0-471-26250-1.
19. ^ Jeffrey Seth Rosenthal (2000). A First Look at Rigorous Probability Theory. World Scientific.
20. ^ Chapter 3.2 of DeGroot & Schervish (2002)
21. ^ Bourne, Murray. "11. Probability Distributions - Concepts". www.intmath.com. Retrieved 2020-09-10.
22. ^ W., Stroock, Daniel (1999). Probability theory : an analytic view (Rev. ed.). Cambridge [England]: Cambridge University Press. p. 11\. ISBN 978-0521663496. OCLC 43953136.
23. ^ Kolmogorov, Andrey (1950) [1933]. Foundations of the theory of probability. New York, USA: Chelsea Publishing Company. pp. 21–24.
24. ^ Joyce, David (2014). "Axioms of Probability" (PDF). Clark University. Retrieved December 5, 2019.
25. ^ a b Alligood, K.T.; Sauer, T.D.; Yorke, J.A. (1996). Chaos: an introduction to dynamical systems. Springer.
26. ^ Rabinovich, M.I.; Fabrikant, A.L. (1979). "Stochastic self-modulation of waves in nonequilibrium media". J. Exp. Theor. Phys. 77: 617–629. Bibcode:1979JETP...50..311R.
27. ^ Section 1.9 of Ross, S.M.; Peköz, E.A. (2007). A second course in probability (PDF).
28. ^ Walters, Peter (2000). An Introduction to Ergodic Theory. Springer.
29. ^ a b c Dekking, Frederik Michel; Kraaikamp, Cornelis; Lopuhaä, Hendrik Paul; Meester, Ludolf Erwin (2005), "Why probability and statistics?", A Modern Introduction to Probability and Statistics, Springer London, pp. 1–11, doi:10.1007/1-84628-168-7_1, ISBN 978-1-85233-896-1
30. ^ Bishop, Christopher M. (2006). Pattern recognition and machine learning. New York: Springer. ISBN 0-387-31073-8. OCLC 71008143.
31. ^ Chang, Raymond. (2014). Physical chemistry for the chemical sciences. Thoman, John W., Jr., 1960-. [Mill Valley, California]. pp. 403–406. ISBN 978-1-68015-835-9. OCLC 927509011.
32. ^ Chen, P.; Chen, Z.; Bak-Jensen, B. (April 2008). "Probabilistic load flow: A review". 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. pp. 1586–1591. doi:10.1109/drpt.2008.4523658. ISBN 978-7-900714-13-8. S2CID 18669309.
33. ^ Maity, Rajib (2018-04-30). Statistical methods in hydrology and hydroclimatology. Singapore. ISBN 978-981-10-8779-0. OCLC 1038418263.

### Sources[edit | edit source]

* den Dekker, A. J.; Sijbers, J. (2014). "Data distributions in magnetic resonance images: A review". Physica Medica. 30 (7): 725–741. doi:10.1016/j.ejmp.2014.05.002. PMID 25059432.
* Vapnik, Vladimir Naumovich (1998). Statistical Learning Theory. John Wiley and Sons.

## External links[edit | edit source]

Wikimedia Commons has media related to Probability distribution.

* "Probability distribution", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
* Field Guide to Continuous Probability Distributions, Gavin E. Crooks.

* v
* t
* e

Probability distributions (List)

Discrete
univariate|

| with finite
support|

* Benford
* Bernoulli
* beta-binomial
* binomial
* categorical
* hypergeometric
* negative
* Poisson binomial
* Rademacher
* soliton
* discrete uniform
* Zipf
* Zipf–Mandelbrot

|

with infinite
support|

* beta negative binomial
* Borel
* Conway–Maxwell–Poisson
* discrete phase-type
* Delaporte
* extended negative binomial
* Flory–Schulz
* Gauss–Kuzmin
* geometric
* logarithmic
* mixed Poisson
* negative binomial
* Panjer
* parabolic fractal
* Poisson
* Skellam
* Yule–Simon
* zeta

Continuous
univariate|

| supported on a
bounded interval|

* arcsine
* ARGUS
* Balding–Nichols
* Bates
* beta
* beta rectangular
* continuous Bernoulli
* Irwin–Hall
* Kumaraswamy
* logit-normal
* noncentral beta
* PERT
* raised cosine
* reciprocal
* triangular
* U-quadratic
* uniform
* Wigner semicircle

|

supported on a
semi-infinite
interval|

* Benini
* Benktander 1st kind
* Benktander 2nd kind
* beta prime
* Burr
* chi
* chi-squared
* noncentral
* inverse
* scaled
* Dagum
* Davis
* Erlang
* hyper
* exponential
* hyperexponential
* hypoexponential
* logarithmic
* F
* noncentral
* folded normal
* Fréchet
* gamma
* generalized
* inverse
* gamma/Gompertz
* Gompertz
* shifted
* half-logistic
* half-normal
* Hotelling's T-squared
* inverse Gaussian
* generalized
* Kolmogorov
* Lévy
* log-Cauchy
* log-Laplace
* log-logistic
* log-normal
* log-t
* Lomax
* matrix-exponential
* Maxwell–Boltzmann
* Maxwell–Jüttner
* Mittag-Leffler
* Nakagami
* Pareto
* phase-type
* Poly-Weibull
* Rayleigh
* relativistic Breit–Wigner
* Rice
* truncated normal
* type-2 Gumbel
* Weibull
* discrete
* Wilks's lambda

supported
on the whole
real line|

* Cauchy
* exponential power
* Fisher's z
* κ-Gaussian
* Gaussian q
* generalized normal
* generalized hyperbolic
* geometric stable
* Gumbel
* Holtsmark
* hyperbolic secant
* Johnson's SU
* Landau
* Laplace
* asymmetric
* logistic
* noncentral t
* normal (Gaussian)
* normal-inverse Gaussian
* skew normal
* slash
* stable
* Student's t
* Tracy–Widom
* variance-gamma
* Voigt

with support
whose type varies|

* generalized chi-squared
* generalized extreme value
* generalized Pareto
* Marchenko–Pastur
* κ-exponential
* κ-Gamma
* κ-Weibull
* κ-Logistic
* κ-Erlangl
* q-exponential
* q-Gaussian
* q-Weibull
* shifted log-logistic
* Tukey lambda

Mixed
univariate|

| continuous-
discrete|

* Rectified Gaussian

|

Multivariate
(joint)|

* Discrete:
* Ewens
* multinomial
* Dirichlet
* negative
* Continuous:
* Dirichlet
* generalized
* multivariate Laplace
* multivariate normal
* multivariate stable
* multivariate t
* normal-gamma
* inverse
* Matrix-valued:
* LKJ
* matrix normal
* matrix t
* matrix gamma
* inverse matrix gamma
* Wishart
* normal
* inverse
* normal-inverse

Directional|

Univariate (circular) directional
Circular uniform
univariate von Mises
wrapped normal
wrapped Cauchy
wrapped exponential
wrapped asymmetric Laplace
wrapped Lévy
Bivariate (spherical)
Kent
Bivariate (toroidal)
bivariate von Mises
Multivariate
von Mises–Fisher
Bingham

Degenerate
and singular|

Degenerate
Dirac delta function
Singular
Cantor

Families|

* Circular
* compound Poisson
* elliptical
* exponential
* natural exponential
* location–scale
* maximum entropy
* mixture
* Pearson
* Tweedie
* wrapped

* Category
* Commons

* v
* t
* e

Theory of probability distributions

* probability mass function (pmf)
* probability density function (pdf)
* cumulative distribution function (cdf)
* quantile function

File:Loglogisticpdf no-labels.svg

* raw moment
* central moment
* mean
* variance
* standard deviation
* skewness
* kurtosis
* L-moment

* moment-generating function (mgf)
* characteristic function
* probability-generating function (pgf)
* cumulant
* combinant

* v
* t
* e

Statistics

* Outline
* Index

Descriptive statistics

| Continuous data|

| Center|

* Mean
* Arithmetic
* Cubic
* Generalized/power
* Geometric
* Harmonic
* Heinz
* Lehmer
* Median
* Mode

|

Dispersion|

* Average absolute deviation
* Coefficient of variation
* Interquartile range
* Percentile
* Range
* Standard deviation
* Variance

Shape|

* Central limit theorem
* Moments
* Kurtosis
* L-moments
* Skewness

Count data|

* Index of dispersion

Summary tables|

* Contingency table
* Frequency distribution
* Grouped data

Dependence|

* Partial correlation
* Pearson product-moment correlation
* Rank correlation
* Kendall's τ
* Spearman's ρ
* Scatter plot

Graphics|

* Bar chart
* Biplot
* Box plot
* Control chart
* Correlogram
* Fan chart
* Forest plot
* Histogram
* Pie chart
* Q–Q plot
* Radar chart
* Run chart
* Scatter plot
* Stem-and-leaf display
* Violin plot

Data collection

| Study design|

* Effect size
* Missing data
* Optimal design
* Population
* Replication
* Sample size determination
* Statistic
* Statistical power

|

Survey methodology|

* Sampling
* Cluster
* Stratified
* Opinion poll
* Questionnaire
* Standard error

Controlled experiments|

* Blocking
* Factorial experiment
* Interaction
* Random assignment
* Randomized controlled trial
* Randomized experiment
* Scientific control

Adaptive designs|

* Adaptive clinical trial
* Stochastic approximation
* Up-and-down designs

Observational studies|

* Cohort study
* Cross-sectional study
* Natural experiment
* Quasi-experiment

Statistical inference

| Statistical theory|

* Population
* Statistic
* Probability distribution
* Sampling distribution
* Order statistic
* Empirical distribution
* Density estimation
* Statistical model
* Model specification
* Lp space
* Parameter
* location
* scale
* shape
* Parametric family
* Likelihood (monotone)
* Location–scale family
* Exponential family
* Completeness
* Sufficiency
* Statistical functional
* Bootstrap
* U
* V
* Optimal decision
* loss function
* Efficiency
* Statistical distance
* divergence
* Asymptotics
* Robustness

|

Frequentist inference|

| Point estimation|

* Estimating equations
* Maximum likelihood
* Method of moments
* M-estimator
* Minimum distance
* Unbiased estimators
* Mean-unbiased minimum-variance
* Rao–Blackwellization
* Lehmann–Scheffé theorem
* Median unbiased
* Plug-in

|

Interval estimation|

* Confidence interval
* Pivot
* Likelihood interval
* Prediction interval
* Tolerance interval
* Resampling
* Bootstrap
* Jackknife

Testing hypotheses|

* 1- & 2-tails
* Power
* Uniformly most powerful test
* Permutation test
* Randomization test
* Multiple comparisons

Parametric tests|

* Likelihood-ratio
* Score/Lagrange multiplier
* Wald

Specific tests|

* Z-test (normal)
* Student's t-test
* F-test

Goodness of fit|

* Chi-squared
* G-test
* Kolmogorov–Smirnov
* Anderson–Darling
* Lilliefors
* Jarque–Bera
* Normality (Shapiro–Wilk)
* Likelihood-ratio test
* Model selection
* Cross validation
* AIC
* BIC

Rank statistics|

* Sign
* Sample median
* Signed rank (Wilcoxon)
* Hodges–Lehmann estimator
* Rank sum (Mann–Whitney)
* Nonparametric anova
* 1-way (Kruskal–Wallis)
* 2-way (Friedman)
* Ordered alternative (Jonckheere–Terpstra)
* Van der Waerden test

Bayesian inference|

* Bayesian probability
* prior
* posterior
* Credible interval
* Bayes factor
* Bayesian estimator
* Maximum posterior estimator

* Correlation
* Regression analysis

| Correlation|

* Pearson product-moment
* Partial correlation
* Confounding variable
* Coefficient of determination

|

Regression analysis|

* Errors and residuals
* Regression validation
* Mixed effects models
* Simultaneous equations models
* Multivariate adaptive regression splines (MARS)

Linear regression|

* Simple linear regression
* Ordinary least squares
* General linear model
* Bayesian regression

Non-standard predictors|

* Nonlinear regression
* Nonparametric
* Semiparametric
* Isotonic
* Robust
* Heteroscedasticity
* Homoscedasticity

Generalized linear model|

* Exponential families
* Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance|

* Analysis of variance (ANOVA, anova)
* Analysis of covariance
* Multivariate ANOVA
* Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

| Categorical|

* Cohen's kappa
* Contingency table
* Graphical model
* Log-linear model
* McNemar's test
* Cochran-Mantel-Haenszel statistics

|

Multivariate|

* Regression
* Manova
* Principal components
* Canonical correlation
* Discriminant analysis
* Cluster analysis
* Classification
* Structural equation model
* Factor analysis
* Multivariate distributions
* Elliptical distributions
* Normal

Time-series|

| General|

* Decomposition
* Trend
* Stationarity
* Seasonal adjustment
* Exponential smoothing
* Cointegration
* Structural break
* Granger causality

|

Specific tests|

* Dickey–Fuller
* Johansen
* Q-statistic (Ljung–Box)
* Durbin–Watson
* Breusch–Godfrey

Time domain|

* Autocorrelation (ACF)
* partial (PACF)
* Cross-correlation (XCF)
* ARMA model
* ARIMA model (Box–Jenkins)
* Autoregressive conditional heteroskedasticity (ARCH)
* Vector autoregression (VAR)

Frequency domain|

* Spectral density estimation
* Fourier analysis
* Least-squares spectral analysis
* Wavelet
* Whittle likelihood

Survival|

| Survival function|

* Kaplan–Meier estimator (product limit)
* Proportional hazards models
* Accelerated failure time (AFT) model
* First hitting time

|

Hazard function|

* Nelson–Aalen estimator

Test|

* Log-rank test

Applications

| Biostatistics|

* Bioinformatics
* Clinical trials / studies
* Epidemiology
* Medical statistics

|

Engineering statistics|

* Chemometrics
* Methods engineering
* Probabilistic design
* Process / quality control
* Reliability
* System identification

Social statistics|

* Actuarial science
* Census
* Crime statistics
* Demography
* Econometrics
* Jurimetrics
* National accounts
* Official statistics
* Population statistics
* Psychometrics

Spatial statistics|

* Cartography
* Environmental statistics
* Geographic information system
* Geostatistics
* Kriging

* Category
* '
* Commons
* Project WikiProject

it:Variabile casuale#Distribuzione di probabilità
*[v]: View this template
*[t]: Discuss this template
*[e]: Edit this template