# Random variable

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In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is described informally as a variable whose values depend on outcomes of a random phenomenon. [1] The formal mathematical treatment of random variables is a topic in probability theory. In that context, a random variable is understood as a measurable function defined on a probability space that maps from the sample space to the real numbers. [2]

## Contents

A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, because of imprecise measurements or quantum uncertainty). [1] They may also conceptually represent either the results of an "objectively" random process (such as rolling a die) or the "subjective" randomness that results from incomplete knowledge of a quantity. The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself, but is instead related to philosophical arguments over the interpretation of probability. The mathematics works the same regardless of the particular interpretation in use.

As a function, a random variable is required to be measurable, which allows for probabilities to be assigned to sets of its potential values. It is common that the outcomes depend on some physical variables that are not predictable. For example, when tossing a fair coin, the final outcome of heads or tails depends on the uncertain physical conditions, so the outcome being observed is uncertain. The coin could get caught in a crack in the floor, but such a possibility is excluded from consideration.

The domain of a random variable is called a sample space, defined as the set of possible outcomes of a non-deterministic event. For example, in the event of a coin toss, only two possible outcomes are possible: heads or tails.

A random variable has a probability distribution, which specifies the probability of Borel subsets of its range. Random variables can be discrete, that is, taking any of a specified finite or countable list of values (having a countable range), endowed with a probability mass function that is characteristic of the random variable's probability distribution; or continuous, taking any numerical value in an interval or collection of intervals (having an uncountable range), via a probability density function that is characteristic of the random variable's probability distribution; or a mixture of both.

Two random variables with the same probability distribution can still differ in terms of their associations with, or independence from, other random variables. The realizations of a random variable, that is, the results of randomly choosing values according to the variable's probability distribution function, are called random variates.

Although the idea was originally introduced by Christiaan Huygens, the first person to think systematically in terms of random variables was Pafnuty Chebyshev. [3] [4]

## Definition

A random variable${\displaystyle X}$ is a measurable function ${\displaystyle X\colon \Omega \to E}$ from a set of possible outcomes ${\displaystyle \Omega }$ to a measurable space ${\displaystyle E}$. The technical axiomatic definition requires ${\displaystyle \Omega }$ to be a sample space of a probability triple ${\displaystyle (\Omega ,{\mathcal {F}},\operatorname {P} )}$ (see the measure-theoretic definition). A random variable is often denoted by capital roman letters such as ${\displaystyle X}$, ${\displaystyle Y}$, ${\displaystyle Z}$, ${\displaystyle T}$. [5] [6]

The probability that ${\displaystyle X}$ takes on a value in a measurable set ${\displaystyle S\subseteq E}$ is written as

${\displaystyle \operatorname {P} (X\in S)=\operatorname {P} (\{\omega \in \Omega \mid X(\omega )\in S\})}$ [5]

### Standard case

In many cases, ${\displaystyle X}$ is real-valued, i.e. ${\displaystyle E=\mathbb {R} }$. In some contexts, the term random element (see extensions) is used to denote a random variable not of this form.

When the image (or range) of ${\displaystyle X}$ is countable, the random variable is called a discrete random variable [7] :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 ${\displaystyle X}$. If the image is uncountably infinite (usually an interval) then ${\displaystyle X}$ is called a continuous random variable. [8] [ citation needed ] In the special case that it is absolutely continuous, its distribution can be described by a probability density function, which assigns probabilities to intervals; in particular, each individual point must necessarily have probability zero for an absolutely continuous random variable. Not all continuous random variables are absolutely continuous, [9] a mixture distribution is one such counterexample; such random variables cannot be described by a probability density or a probability mass function.

Any random variable can be described by its cumulative distribution function, which describes the probability that the random variable will be less than or equal to a certain value.

### Extensions

The term "random variable" in statistics is traditionally limited to the real-valued case (${\displaystyle E=\mathbb {R} }$). In this case, the structure of the real numbers makes it possible to define quantities such as the expected value and variance of a random variable, its cumulative distribution function, and the moments of its distribution.

However, the definition above is valid for any measurable space ${\displaystyle E}$ of values. Thus one can consider random elements of other sets ${\displaystyle E}$, such as random boolean values, categorical values, complex numbers, vectors, matrices, sequences, trees, sets, shapes, manifolds, and functions. One may then specifically refer to a random variable of type ${\displaystyle E}$, or an ${\displaystyle E}$-valued random variable.

This more general concept of a random element is particularly useful in disciplines such as graph theory, machine learning, natural language processing, and other fields in discrete mathematics and computer science, where one is often interested in modeling the random variation of non-numerical data structures. In some cases, it is nonetheless convenient to represent each element of ${\displaystyle E}$, using one or more real numbers. In this case, a random element may optionally be represented as a vector of real-valued random variables (all defined on the same underlying probability space ${\displaystyle \Omega }$, which allows the different random variables to covary). For example:

• A random word may be represented as a random integer that serves as an index into the vocabulary of possible words. Alternatively, it can be represented as a random indicator vector, whose length equals the size of the vocabulary, where the only values of positive probability are ${\displaystyle (1\ 0\ 0\ 0\ \cdots )}$, ${\displaystyle (0\ 1\ 0\ 0\ \cdots )}$, ${\displaystyle (0\ 0\ 1\ 0\ \cdots )}$ and the position of the 1 indicates the word.
• A random sentence of given length ${\displaystyle N}$ may be represented as a vector of ${\displaystyle N}$ random words.
• A random graph on ${\displaystyle N}$ given vertices may be represented as a ${\displaystyle N\times N}$ matrix of random variables, whose values specify the adjacency matrix of the random graph.
• A random function ${\displaystyle F}$ may be represented as a collection of random variables ${\displaystyle F(x)}$, giving the function's values at the various points ${\displaystyle x}$ in the function's domain. The ${\displaystyle F(x)}$ are ordinary real-valued random variables provided that the function is real-valued. For example, a stochastic process is a random function of time, a random vector is a random function of some index set such as ${\displaystyle 1,2,\ldots ,n}$, and random field is a random function on any set (typically time, space, or a discrete set).

## Distribution functions

If a random variable ${\displaystyle X\colon \Omega \to \mathbb {R} }$ defined on the probability space ${\displaystyle (\Omega ,{\mathcal {F}},\operatorname {P} )}$ is given, we can ask questions like "How likely is it that the value of ${\displaystyle X}$ is equal to 2?". This is the same as the probability of the event ${\displaystyle \{\omega :X(\omega )=2\}\,\!}$ which is often written as ${\displaystyle P(X=2)\,\!}$ or ${\displaystyle p_{X}(2)}$ for short.

Recording all these probabilities of output ranges of a real-valued random variable ${\displaystyle X}$ yields the probability distribution of ${\displaystyle X}$. The probability distribution "forgets" about the particular probability space used to define ${\displaystyle X}$ and only records the probabilities of various values of ${\displaystyle X}$. Such a probability distribution can always be captured by its cumulative distribution function

${\displaystyle F_{X}(x)=\operatorname {P} (X\leq x)}$

and sometimes also using a probability density function, ${\displaystyle p_{X}}$. In measure-theoretic terms, we use the random variable ${\displaystyle X}$ to "push-forward" the measure ${\displaystyle P}$ on ${\displaystyle \Omega }$ to a measure ${\displaystyle p_{X}}$ on ${\displaystyle \mathbb {R} }$. The underlying probability space ${\displaystyle \Omega }$ is a technical device used to guarantee the existence of random variables, sometimes to construct them, and to define notions such as correlation and dependence or independence based on a joint distribution of two or more random variables on the same probability space. In practice, one often disposes of the space ${\displaystyle \Omega }$ altogether and just puts a measure on ${\displaystyle \mathbb {R} }$ that assigns measure 1 to the whole real line, i.e., one works with probability distributions instead of random variables. See the article on quantile functions for fuller development.

## Examples

### Discrete random variable

In an experiment a person may be chosen at random, and one random variable may be the person's height. Mathematically, the random variable is interpreted as a function which maps the person to the person's height. Associated with the random variable is a probability distribution that allows the computation of the probability that the height is in any subset of possible values, such as the probability that the height is between 180 and 190 cm, or the probability that the height is either less than 150 or more than 200 cm.

Another random variable may be the person's number of children; this is a discrete random variable with non-negative integer values. It allows the computation of probabilities for individual integer values – the probability mass function (PMF) – or for sets of values, including infinite sets. For example, the event of interest may be "an even number of children". For both finite and infinite event sets, their probabilities can be found by adding up the PMFs of the elements; that is, the probability of an even number of children is the infinite sum ${\displaystyle \operatorname {PMF} (0)+\operatorname {PMF} (2)+\operatorname {PMF} (4)+\cdots }$.

In examples such as these, the sample space is often suppressed, since it is mathematically hard to describe, and the possible values of the random variables are then treated as a sample space. But when two random variables are measured on the same sample space of outcomes, such as the height and number of children being computed on the same random persons, it is easier to track their relationship if it is acknowledged that both height and number of children come from the same random person, for example so that questions of whether such random variables are correlated or not can be posed.

If ${\textstyle \{a_{n}\},\{b_{n}\}}$ are countable sets of real numbers, ${\textstyle b_{n}>0}$ and ${\displaystyle \sum _{n}b_{n}=1}$, then ${\displaystyle F=\sum _{n}b_{n}\delta _{a_{n}}}$ is a discrete distribution function. Here ${\displaystyle \delta _{t}(x)=0}$ for ${\displaystyle x, ${\displaystyle \delta _{t}(x)=1}$ for ${\displaystyle x\geq t}$. Taking for instance an enumeration of all rational numbers as ${\displaystyle \{a_{n}\}}$, one gets a discrete distribution function that is not a step function or piecewise constant. [7]

#### Coin toss

The possible outcomes for one coin toss can be described by the sample space ${\displaystyle \Omega =\{{\text{heads}},{\text{tails}}\}}$. We can introduce a real-valued random variable ${\displaystyle Y}$ that models a \$1 payoff for a successful bet on heads as follows:

${\displaystyle Y(\omega )={\begin{cases}1,&{\text{if }}\omega ={\text{heads}},\\[6pt]0,&{\text{if }}\omega ={\text{tails}}.\end{cases}}}$

If the coin is a fair coin, Y has a probability mass function ${\displaystyle f_{Y}}$ given by:

${\displaystyle f_{Y}(y)={\begin{cases}{\tfrac {1}{2}},&{\text{if }}y=1,\\[6pt]{\tfrac {1}{2}},&{\text{if }}y=0,\end{cases}}}$

#### Dice roll

A random variable can also be used to describe the process of rolling dice and the possible outcomes. The most obvious representation for the two-dice case is to take the set of pairs of numbers n1 and n2 from {1, 2, 3, 4, 5, 6} (representing the numbers on the two dice) as the sample space. The total number rolled (the sum of the numbers in each pair) is then a random variable X given by the function that maps the pair to the sum:

${\displaystyle X((n_{1},n_{2}))=n_{1}+n_{2}}$

and (if the dice are fair) has a probability mass function ƒX given by:

${\displaystyle f_{X}(S)={\frac {\min(S-1,13-S)}{36}},{\text{ for }}S\in \{2,3,4,5,6,7,8,9,10,11,12\}}$

### Continuous random variable

Formally, a continuous random variable is a random variable whose cumulative distribution function is continuous everywhere. [10] There are no "gaps", which would correspond to numbers which have a finite probability of occurring. Instead, continuous random variables almost never take an exact prescribed value c (formally, ${\textstyle \forall c\in \mathbb {R}$ :\;\Pr(X=c)=0}) but there is a positive probability that its value will lie in particular intervals which can be arbitrarily small. Continuous random variables usually admit probability density functions (PDF), which characterize their CDF and probability measures; such distributions are also called absolutely continuous; but some continuous distributions are singular, or mixes of an absolutely continuous part and a singular part.

An example of a continuous random variable would be one based on a spinner that can choose a horizontal direction. Then the values taken by the random variable are directions. We could represent these directions by North, West, East, South, Southeast, etc. However, it is commonly more convenient to map the sample space to a random variable which takes values which are real numbers. This can be done, for example, by mapping a direction to a bearing in degrees clockwise from North. The random variable then takes values which are real numbers from the interval [0, 360), with all parts of the range being "equally likely". In this case, X = the angle spun. Any real number has probability zero of being selected, but a positive probability can be assigned to any range of values. For example, the probability of choosing a number in [0, 180] is 12. Instead of speaking of a probability mass function, we say that the probability density of X is 1/360. The probability of a subset of [0, 360) can be calculated by multiplying the measure of the set by 1/360. In general, the probability of a set for a given continuous random variable can be calculated by integrating the density over the given set.

More formally, given any interval ${\textstyle I=[a,b]=\{x\in \mathbb {R} :a\leq x\leq b\}}$, a random variable ${\displaystyle X_{I}\sim \operatorname {U} (I)=\operatorname {U} [a,b]}$ is called a "continuous uniform random variable" (CURV) if the probability that it takes a value in a subinterval depends only on the length of the subinterval. This implies that the probability of ${\displaystyle X_{I}}$ falling in any subinterval ${\displaystyle [c,d]\subseteq [a,b]}$ is proportional to the length of the subinterval, that is, if acdb, one has

${\displaystyle \Pr \left(X_{I}\in [c,d]\right)={\frac {d-c}{b-a}}\Pr \left(X_{I}\in I\right)={\frac {d-c}{b-a}}}$

where the last equality results from the unitarity axiom of probability. The probability density function of a CURV ${\displaystyle X\sim \operatorname {U} [a,b]}$ is given by the indicator function of its interval of support normalized by the interval's length:

${\displaystyle f_{X}(x)={\begin{cases}\displaystyle {1 \over b-a},&a\leq x\leq b\\0,&{\text{otherwise}}.\end{cases}}}$

Of particular interest is the uniform distribution on the unit interval ${\displaystyle [0,1]}$. Samples of any desired probability distribution ${\displaystyle \operatorname {D} }$ can be generated by calculating the quantile function of ${\displaystyle \operatorname {D} }$ on a randomly-generated number distributed uniformly on the unit interval. This exploits properties of cumulative distribution functions, which are a unifying framework for all random variables.

### Mixed type

A mixed random variable is a random variable whose cumulative distribution function is neither piecewise-constant (a discrete random variable) nor everywhere-continuous. [10] It can be realized as the sum of a discrete random variable and a continuous random variable; in which case the CDF will be the weighted average of the CDFs of the component variables. [10]

An example of a random variable of mixed type would be based on an experiment where a coin is flipped and the spinner is spun only if the result of the coin toss is heads. If the result is tails, X = −1; otherwise X = the value of the spinner as in the preceding example. There is a probability of 12 that this random variable will have the value −1. Other ranges of values would have half the probabilities of the last example.

Most generally, every probability distribution on the real line is a mixture of discrete part, singular part, and an absolutely continuous part; see Lebesgue's decomposition theorem § Refinement. The discrete part is concentrated on a countable set, but this set may be dense (like the set of all rational numbers).

## Measure-theoretic definition

The most formal, axiomatic definition of a random variable involves measure theory. Continuous random variables are defined in terms of sets of numbers, along with functions that map such sets to probabilities. Because of various difficulties (e.g. the Banach–Tarski paradox) that arise if such sets are insufficiently constrained, it is necessary to introduce what is termed a sigma-algebra to constrain the possible sets over which probabilities can be defined. Normally, a particular such sigma-algebra is used, the Borel σ-algebra, which allows for probabilities to be defined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals. [2]

The measure-theoretic definition is as follows.

Let ${\displaystyle (\Omega ,{\mathcal {F}},P)}$ be a probability space and ${\displaystyle (E,{\mathcal {E}})}$ a measurable space. Then an ${\displaystyle (E,{\mathcal {E}})}$-valued random variable is a measurable function ${\displaystyle X\colon \Omega \to E}$, which means that, for every subset ${\displaystyle B\in {\mathcal {E}}}$, its preimage is ${\displaystyle {\mathcal {F}}}$-measurable; ${\displaystyle X^{-1}(B)\in {\mathcal {F}}}$, where ${\displaystyle X^{-1}(B)=\{\omega :X(\omega )\in B\}}$. [11] This definition enables us to measure any subset ${\displaystyle B\in {\mathcal {E}}}$ in the target space by looking at its preimage, which by assumption is measurable.

In more intuitive terms, a member of ${\displaystyle \Omega }$ is a possible outcome, a member of ${\displaystyle {\mathcal {F}}}$ is a measurable subset of possible outcomes, the function ${\displaystyle P}$ gives the probability of each such measurable subset, ${\displaystyle E}$ represents the set of values that the random variable can take (such as the set of real numbers), and a member of ${\displaystyle {\mathcal {E}}}$ is a "well-behaved" (measurable) subset of ${\displaystyle E}$ (those for which the probability may be determined). The random variable is then a function from any outcome to a quantity, such that the outcomes leading to any useful subset of quantities for the random variable have a well-defined probability.

When ${\displaystyle E}$ is a topological space, then the most common choice for the σ-algebra ${\displaystyle {\mathcal {E}}}$ is the Borel σ-algebra ${\displaystyle {\mathcal {B}}(E)}$, which is the σ-algebra generated by the collection of all open sets in ${\displaystyle E}$. In such case the ${\displaystyle (E,{\mathcal {E}})}$-valued random variable is called an ${\displaystyle E}$-valued random variable. Moreover, when the space ${\displaystyle E}$ is the real line ${\displaystyle \mathbb {R} }$, then such a real-valued random variable is called simply a random variable.

### Real-valued random variables

In this case the observation space is the set of real numbers. Recall, ${\displaystyle (\Omega ,{\mathcal {F}},P)}$ is the probability space. For a real observation space, the function ${\displaystyle X\colon \Omega \rightarrow \mathbb {R} }$ is a real-valued random variable if

${\displaystyle \{\omega :X(\omega )\leq r\}\in {\mathcal {F}}\qquad \forall r\in \mathbb {R} .}$

This definition is a special case of the above because the set ${\displaystyle \{(-\infty ,r]:r\in \mathbb {R} \}}$ generates the Borel σ-algebra on the set of real numbers, and it suffices to check measurability on any generating set. Here we can prove measurability on this generating set by using the fact that ${\displaystyle \{\omega :X(\omega )\leq r\}=X^{-1}((-\infty ,r])}$.

## Moments

The probability distribution of a random variable is often characterised by a small number of parameters, which also have a practical interpretation. For example, it is often enough to know what its "average value" is. This is captured by the mathematical concept of expected value of a random variable, denoted ${\displaystyle \operatorname {E} [X]}$, and also called the first moment. In general, ${\displaystyle \operatorname {E} [f(X)]}$ is not equal to ${\displaystyle f(\operatorname {E} [X])}$. Once the "average value" is known, one could then ask how far from this average value the values of ${\displaystyle X}$ typically are, a question that is answered by the variance and standard deviation of a random variable. ${\displaystyle \operatorname {E} [X]}$ can be viewed intuitively as an average obtained from an infinite population, the members of which are particular evaluations of ${\displaystyle X}$.

Mathematically, this is known as the (generalised) problem of moments: for a given class of random variables ${\displaystyle X}$, find a collection ${\displaystyle \{f_{i}\}}$ of functions such that the expectation values ${\displaystyle \operatorname {E} [f_{i}(X)]}$ fully characterise the distribution of the random variable ${\displaystyle X}$.

Moments can only be defined for real-valued functions of random variables (or complex-valued, etc.). If the random variable is itself real-valued, then moments of the variable itself can be taken, which are equivalent to moments of the identity function ${\displaystyle f(X)=X}$ of the random variable. However, even for non-real-valued random variables, moments can be taken of real-valued functions of those variables. For example, for a categorical random variable X that can take on the nominal values "red", "blue" or "green", the real-valued function ${\displaystyle [X={\text{green}}]}$ can be constructed; this uses the Iverson bracket, and has the value 1 if ${\displaystyle X}$ has the value "green", 0 otherwise. Then, the expected value and other moments of this function can be determined.

## Functions of random variables

A new random variable Y can be defined by applying a real Borel measurable function ${\displaystyle g\colon \mathbb {R} \rightarrow \mathbb {R} }$ to the outcomes of a real-valued random variable ${\displaystyle X}$. That is, ${\displaystyle Y=g(X)}$. The cumulative distribution function of ${\displaystyle Y}$ is then

${\displaystyle F_{Y}(y)=\operatorname {P} (g(X)\leq y).}$

If function ${\displaystyle g}$ is invertible (i.e., ${\displaystyle h=g^{-1}}$ exists, where ${\displaystyle h}$ is ${\displaystyle g}$'s inverse function) and is either increasing or decreasing, then the previous relation can be extended to obtain

${\displaystyle F_{Y}(y)=\operatorname {P} (g(X)\leq y)={\begin{cases}\operatorname {P} (X\leq h(y))=F_{X}(h(y)),&{\text{if }}h=g^{-1}{\text{ increasing}},\\\\\operatorname {P} (X\geq h(y))=1-F_{X}(h(y)),&{\text{if }}h=g^{-1}{\text{ decreasing}}.\end{cases}}}$

With the same hypotheses of invertibility of ${\displaystyle g}$, assuming also differentiability, the relation between the probability density functions can be found by differentiating both sides of the above expression with respect to ${\displaystyle y}$, in order to obtain [10]

${\displaystyle f_{Y}(y)=f_{X}{\bigl (}h(y){\bigr )}\left|{\frac {dh(y)}{dy}}\right|.}$

If there is no invertibility of ${\displaystyle g}$ but each ${\displaystyle y}$ admits at most a countable number of roots (i.e., a finite, or countably infinite, number of ${\displaystyle x_{i}}$ such that ${\displaystyle y=g(x_{i})}$) then the previous relation between the probability density functions can be generalized with

${\displaystyle f_{Y}(y)=\sum _{i}f_{X}(g_{i}^{-1}(y))\left|{\frac {dg_{i}^{-1}(y)}{dy}}\right|}$

where ${\displaystyle x_{i}=g_{i}^{-1}(y)}$, according to the inverse function theorem. The formulas for densities do not demand ${\displaystyle g}$ to be increasing.

In the measure-theoretic, axiomatic approach to probability, if a random variable ${\displaystyle X}$ on ${\displaystyle \Omega }$ and a Borel measurable function ${\displaystyle g\colon \mathbb {R} \rightarrow \mathbb {R} }$, then ${\displaystyle Y=g(X)}$ is also a random variable on ${\displaystyle \Omega }$, since the composition of measurable functions is also measurable. (However, this is not necessarily true if ${\displaystyle g}$ is Lebesgue measurable.[ citation needed ]) The same procedure that allowed one to go from a probability space ${\displaystyle (\Omega ,P)}$ to ${\displaystyle (\mathbb {R} ,dF_{X})}$ can be used to obtain the distribution of ${\displaystyle Y}$.

### Example 1

Let ${\displaystyle X}$ be a real-valued, continuous random variable and let ${\displaystyle Y=X^{2}}$.

${\displaystyle F_{Y}(y)=\operatorname {P} (X^{2}\leq y).}$

If ${\displaystyle y<0}$, then ${\displaystyle P(X^{2}\leq y)=0}$, so

${\displaystyle F_{Y}(y)=0\qquad {\hbox{if}}\quad y<0.}$

If ${\displaystyle y\geq 0}$, then

${\displaystyle \operatorname {P} (X^{2}\leq y)=\operatorname {P} (|X|\leq {\sqrt {y}})=\operatorname {P} (-{\sqrt {y}}\leq X\leq {\sqrt {y}}),}$

so

${\displaystyle F_{Y}(y)=F_{X}({\sqrt {y}})-F_{X}(-{\sqrt {y}})\qquad {\hbox{if}}\quad y\geq 0.}$

### Example 2

Suppose ${\displaystyle X}$ is a random variable with a cumulative distribution

${\displaystyle F_{X}(x)=P(X\leq x)={\frac {1}{(1+e^{-x})^{\theta }}}}$

where ${\displaystyle \theta >0}$ is a fixed parameter. Consider the random variable ${\displaystyle Y=\mathrm {log} (1+e^{-X}).}$ Then,

${\displaystyle F_{Y}(y)=P(Y\leq y)=P(\mathrm {log} (1+e^{-X})\leq y)=P(X\geq -\mathrm {log} (e^{y}-1)).\,}$

The last expression can be calculated in terms of the cumulative distribution of ${\displaystyle X,}$ so

{\displaystyle {\begin{aligned}F_{Y}(y)&=1-F_{X}(-\log(e^{y}-1))\\[5pt]&=1-{\frac {1}{(1+e^{\log(e^{y}-1)})^{\theta }}}\\[5pt]&=1-{\frac {1}{(1+e^{y}-1)^{\theta }}}\\[5pt]&=1-e^{-y\theta }.\end{aligned}}}

which is the cumulative distribution function (CDF) of an exponential distribution.

### Example 3

Suppose ${\displaystyle X}$ is a random variable with a standard normal distribution, whose density is

${\displaystyle f_{X}(x)={\frac {1}{\sqrt {2\pi }}}e^{-x^{2}/2}.}$

Consider the random variable ${\displaystyle Y=X^{2}.}$ We can find the density using the above formula for a change of variables:

${\displaystyle f_{Y}(y)=\sum _{i}f_{X}(g_{i}^{-1}(y))\left|{\frac {dg_{i}^{-1}(y)}{dy}}\right|.}$

In this case the change is not monotonic, because every value of ${\displaystyle Y}$ has two corresponding values of ${\displaystyle X}$ (one positive and negative). However, because of symmetry, both halves will transform identically, i.e.,

${\displaystyle f_{Y}(y)=2f_{X}(g^{-1}(y))\left|{\frac {dg^{-1}(y)}{dy}}\right|.}$

The inverse transformation is

${\displaystyle x=g^{-1}(y)={\sqrt {y}}}$

and its derivative is

${\displaystyle {\frac {dg^{-1}(y)}{dy}}={\frac {1}{2{\sqrt {y}}}}.}$

Then,

${\displaystyle f_{Y}(y)=2{\frac {1}{\sqrt {2\pi }}}e^{-y/2}{\frac {1}{2{\sqrt {y}}}}={\frac {1}{\sqrt {2\pi y}}}e^{-y/2}.}$

This is a chi-squared distribution with one degree of freedom.

### Example 4

Suppose ${\displaystyle X}$ is a random variable with a normal distribution, whose density is

${\displaystyle f_{X}(x)={\frac {1}{\sqrt {2\pi \sigma ^{2}}}}e^{-(x-\mu )^{2}/(2\sigma ^{2})}.}$

Consider the random variable ${\displaystyle Y=X^{2}.}$ We can find the density using the above formula for a change of variables:

${\displaystyle f_{Y}(y)=\sum _{i}f_{X}(g_{i}^{-1}(y))\left|{\frac {dg_{i}^{-1}(y)}{dy}}\right|.}$

In this case the change is not monotonic, because every value of ${\displaystyle Y}$ has two corresponding values of ${\displaystyle X}$ (one positive and negative). Differently from the previous example, in this case however, there is no symmetry and we have to compute the two distinct terms:

${\displaystyle f_{Y}(y)=f_{X}(g_{1}^{-1}(y))\left|{\frac {dg_{1}^{-1}(y)}{dy}}\right|+f_{X}(g_{2}^{-1}(y))\left|{\frac {dg_{2}^{-1}(y)}{dy}}\right|.}$

The inverse transformation is

${\displaystyle x=g_{1,2}^{-1}(y)=\pm {\sqrt {y}}}$

and its derivative is

${\displaystyle {\frac {dg_{1,2}^{-1}(y)}{dy}}=\pm {\frac {1}{2{\sqrt {y}}}}.}$

Then,

${\displaystyle f_{Y}(y)={\frac {1}{\sqrt {2\pi \sigma ^{2}}}}{\frac {1}{2{\sqrt {y}}}}(e^{-({\sqrt {y}}-\mu )^{2}/(2\sigma ^{2})}+e^{-(-{\sqrt {y}}-\mu )^{2}/(2\sigma ^{2})}).}$

This is a noncentral chi-squared distribution with one degree of freedom.

## Some properties

• The probability distribution of the sum of two independent random variables is the convolution of each of their distributions.
• Probability distributions are not a vector space—they are not closed under linear combinations, as these do not preserve non-negativity or total integral 1—but they are closed under convex combination, thus forming a convex subset of the space of functions (or measures).

## Equivalence of random variables

There are several different senses in which random variables can be considered to be equivalent. Two random variables can be equal, equal almost surely, or equal in distribution.

In increasing order of strength, the precise definition of these notions of equivalence is given below.

### Equality in distribution

If the sample space is a subset of the real line, random variables X and Y are equal in distribution (denoted ${\displaystyle X{\stackrel {d}{=}}Y}$) if they have the same distribution functions:

${\displaystyle \operatorname {P} (X\leq x)=\operatorname {P} (Y\leq x)\quad {\text{for all }}x.}$

To be equal in distribution, random variables need not be defined on the same probability space. Two random variables having equal moment generating functions have the same distribution. This provides, for example, a useful method of checking equality of certain functions of independent, identically distributed (IID) random variables. However, the moment generating function exists only for distributions that have a defined Laplace transform.

### Almost sure equality

Two random variables X and Y are equal almost surely (denoted ${\displaystyle X\;{\stackrel {\text{a.s.}}{=}}\;Y}$) if, and only if, the probability that they are different is zero:

${\displaystyle \operatorname {P} (X\neq Y)=0.}$

For all practical purposes in probability theory, this notion of equivalence is as strong as actual equality. It is associated to the following distance:

${\displaystyle d_{\infty }(X,Y)=\operatorname {ess} \sup _{\omega }|X(\omega )-Y(\omega )|,}$

where "ess sup" represents the essential supremum in the sense of measure theory.

### Equality

Finally, the two random variables X and Y are equal if they are equal as functions on their measurable space:

${\displaystyle X(\omega )=Y(\omega )\qquad {\hbox{for all }}\omega .}$

This notion is typically the least useful in probability theory because in practice and in theory, the underlying measure space of the experiment is rarely explicitly characterized or even characterizable.

## Convergence

A significant theme in mathematical statistics consists of obtaining convergence results for certain sequences of random variables; for instance the law of large numbers and the central limit theorem.

There are various senses in which a sequence ${\displaystyle X_{n}}$ of random variables can converge to a random variable ${\displaystyle X}$. These are explained in the article on convergence of random variables.

## Related Research Articles

In probability theory, the expected value of a random variable , denoted or , is a generalization of the weighted average, and is intuitively the arithmetic mean of a large number of independent realizations of . The expected value is also known as the expectation, mathematical expectation, mean, average, or first moment. Expected value is a key concept in economics, finance, and many other subjects.

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. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events.

Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.

In probability theory, the central limit theorem (CLT) establishes that, in many situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution even if the original variables themselves are not normally distributed. The theorem is a key concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions. This theorem has seen many changes during the formal development of probability theory. Previous versions of the theorem date back to 1811, but in its modern general form, this fundamental result in probability theory was precisely stated as late as 1920, thereby serving as a bridge between classical and modern probability theory.

In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample in the sample space can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample. In other words, while the absolute likelihood for a continuous random variable to take on any particular value is 0, the value of the PDF at two different samples can be used to infer, in any particular draw of the random variable, how much more likely it is that the random variable would equal one sample compared to the other sample.

In probability theory, there exist several different notions of convergence of random variables. The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes. The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behavior that is essentially unchanging when items far enough into the sequence are studied. The different possible notions of convergence relate to how such a behavior can be characterized: two readily understood behaviors are that the sequence eventually takes a constant value, and that values in the sequence continue to change but can be described by an unchanging probability distribution.

In probability theory, Chebyshev's inequality guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. Specifically, no more than 1/k2 of the distribution's values can be k or more standard deviations away from the mean. The rule is often called Chebyshev's theorem, about the range of standard deviations around the mean, in statistics. The inequality has great utility because it can be applied to any probability distribution in which the mean and variance are defined. For example, it can be used to prove the weak law of large numbers.

In mathematics, Jensen's inequality, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906. Given its generality, the inequality appears in many forms depending on the context, some of which are presented below. In its simplest form the inequality states that the convex transformation of a mean is less than or equal to the mean applied after convex transformation; it is a simple corollary that the opposite is true of concave transformations.

In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value – the value it would take “on average” over an arbitrarily large number of occurrences – given that a certain set of "conditions" is known to occur. If the random variable can take on only a finite number of values, the “conditions” are that the variable can only take on a subset of those values. More formally, in the case when the random variable is defined over a discrete probability space, the "conditions" are a partition of this probability space.

In probability theory and statistics, given two jointly distributed random variables and , the conditional probability distribution of Y given X is the probability distribution of when is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value of as a parameter. When both and are categorical variables, a conditional probability table is typically used to represent the conditional probability. The conditional distribution contrasts with the marginal distribution of a random variable, which is its distribution without reference to the value of the other variable.

A Dynkin system, named after Eugene Dynkin, is a collection of subsets of another universal set satisfying a set of axioms weaker than those of σ-algebra. Dynkin systems are sometimes referred to as λ-systems or d-system. These set families have applications in measure theory and probability.

In mathematics, probabilistic metric spaces is a generalizations of metric spaces where the distance no longer takes values in the non-negative real numbersc R0, but in distribution functions.

In mathematics, a π-system on a set is a collection of certain subsets of such that

In probability theory, random element is a generalization of the concept of random variable to more complicated spaces than the simple real line. The concept was introduced by Maurice Fréchet (1948) who commented that the “development of probability theory and expansion of area of its applications have led to necessity to pass from schemes where (random) outcomes of experiments can be described by number or a finite set of numbers, to schemes where outcomes of experiments represent, for example, vectors, functions, processes, fields, series, transformations, and also sets or collections of sets.”

In probability theory, the probability integral transform relates to the result that data values that are modeled as being random variables from any given continuous distribution can be converted to random variables having a standard uniform distribution. This holds exactly provided that the distribution being used is the true distribution of the random variables; if the distribution is one fitted to the data, the result will hold approximately in large samples.

In mathematics, uniform integrability is an important concept in real analysis, functional analysis and measure theory, and plays a vital role in the theory of martingales. The definition used in measure theory is closely related to, but not identical to, the definition typically used in probability.

In probability theory, a random measure is a measure-valued random element. Random measures are for example used in the theory of random processes, where they form many important point processes such as Poisson point processes and Cox processes.

In probability theory, the Doob–Dynkin lemma, named after Joseph L. Doob and Eugene Dynkin, characterizes the situation when one random variable is a function of another by the inclusion of the -algebras generated by the random variables. The usual statement of the lemma is formulated in terms of one random variable being measurable with respect to the -algebra generated by the other.

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

In probability theory and statistics, complex random variables are a generalization of real-valued random variables to complex numbers, i.e. the possible values a complex random variable may take are complex numbers. Complex random variables can always be considered as pairs of real random variables: their real and imaginary parts. Therefore, the distribution of one complex random variable may be interpreted as the joint distribution of two real random variables.

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