### Joint distribution

**
**

In the study of probability, given at least two random variables *X*, *Y*, ..., that are defined on a probability space, the **joint probability distribution** for *X*, *Y*, ... is a probability distribution that gives the probability that each of *X*, *Y*, ... falls in any particular range or discrete set of values specified for that variable. In the case of only two random variables, this is called a **bivariate distribution**, but the concept generalizes to any number of random variables, giving a **multivariate distribution**.

The joint probability distribution can be expressed either in terms of a joint cumulative distribution function or in terms of a joint probability density function (in the case of continuous variables) or joint probability mass function (in the case of discrete variables). These in turn can be used to find two other types of distributions: the marginal distribution giving the probabilities for any one of the variables with no reference to any specific ranges of values for the other variables, and the conditional probability distribution giving the probabilities for any subset of the variables conditional on particular values of the remaining variables.

## Contents

## Example

Consider the roll of a die and let *A* = 1 if the number is even (i.e. 2, 4, or 6) and *A* = 0 otherwise. Furthermore, let *B* = 1 if the number is prime (i.e. 2, 3, or 5) and *B* = 0 otherwise.

1 | 2 | 3 | 4 | 5 | 6 | |
---|---|---|---|---|---|---|

A | 0 | 1 | 0 | 1 | 0 | 1 |

B | 0 | 1 | 1 | 0 | 1 | 0 |

Then, the joint distribution of *A* and *B*, expressed as a probability mass function, is

- $$

\mathrm{P}(A=0,B=0)=P\{1\}=\frac{1}{6},\; \mathrm{P}(A=1,B=0)=P\{4,6\}=\frac{2}{6},

- $$

\mathrm{P}(A=0,B=1)=P\{3,5\}=\frac{2}{6},\; \mathrm{P}(A=1,B=1)=P\{2\}=\frac{1}{6}.

These probabilities necessarily sum to 1, since the probability of *some* combination of *A* and *B* occurring is 1.

## Important named distributions

Named joint distributions that arise frequently in statistics include the multivariate normal distribution, the multivariate stable distribution, the multinomial distribution, the negative multinomial distribution, the multivariate hypergeometric distribution, and the elliptical distribution.

## Cumulative distribution

The joint probability distribution for a pair of random variables can be expressed in terms of their cumulative distribution function $F(x,y)=P(X\; \backslash le\; x,\; Y\; \backslash le\; y).$

## Density function or mass function

### Discrete case

The joint probability mass function of two discrete random variables is equal to:

- $$

\begin{align}

\mathrm{P}(X=x\ \mathrm{and}\ Y=y) = \mathrm{P}(Y=y \mid X=x) \cdot \mathrm{P}(X=x) = \mathrm{P}(X=x \mid Y=y) \cdot \mathrm{P}(Y=y) \end{align}.

In general, the joint probability distribution of $n\backslash ,$ discrete random variables $X\_1,\; X\_2,\; \backslash dots,X\_n$ is equal to:

- $$

\begin{align}

\mathrm{P}(X_1=x_1,\dots,X_n=x_n) & = \mathrm{P}(X_1=x_1) \\ & \qquad \times \mathrm{P}(X_2=x_2\mid X_1=x_1) \\ & \quad \qquad \times \mathrm{P}(X_3=x_3\mid X_1=x_1,X_2=x_2) \times \dots \times P(X_n=x_n\mid X_1=x_1,X_2=x_2,\dots,X_{n-1}=x_{n-1})

\end{align}

This identity is known as the chain rule of probability.

Since these are probabilities, we have in the two-variable case

- $\backslash sum\_i\; \backslash sum\_j\; \backslash mathrm\{P\}(X=x\_i\backslash \; \backslash mathrm\{and\}\backslash \; Y=y\_j)\; =\; 1,\backslash ,$

which generalizes for $n\backslash ,$ discrete random variables $X\_1,\; X\_2,\; \backslash dots\; ,\; X\_n$ to

- $\backslash sum\_\{i\}\; \backslash sum\_\{j\}\; \backslash dots\; \backslash sum\_\{k\}\; \backslash mathrm\{P\}(X\_1=x\_\{1i\},X\_2=x\_\{2j\},\; \backslash dots,\; X\_n=x\_\{nk\})\; =\; 1.\backslash ;$

### Continuous case

The **joint probability density function** *f*_{X,Y}(*x*, *y*) for continuous random variables is equal to:

- $f\_\{X,Y\}(x,y)\; =\; f\_\{Y\backslash mid\; X\}(y|x)f\_X(x)\; =\; f\_\{X\backslash mid\; Y\}(x\backslash mid\; y)f\_Y(y)\backslash ;$

…where *f*_{Y|X}(*y*|*x*) and *f*_{X|Y}(*x*|*y*) give the conditional distributions of *Y* given *X* = *x* and of *X* given *Y* = *y* respectively, and *f*_{X}(*x*) and *f*_{Y}(*y*) give the marginal distributions for *X* and *Y* respectively.

Again, since these are probability distributions, one has

- $\backslash int\_x\; \backslash int\_y\; f\_\{X,Y\}(x,y)\; \backslash ;\; dy\; \backslash ;\; dx=\; 1.$

### Mixed case

The "mixed joint density" may be defined in the few cases in which one random variable *X* is continuous but the other random variable *Y* is discrete, or vice versa, as:

- $$

\begin{align}
f_{X,Y}(x,y) = f_{X \mid Y}(x \mid y)\mathrm{P}(Y=y)= \mathrm{P}(Y=y \mid X=x) f_X(x)
\end{align}
One example of a situation in which one may wish to find the cumulative distribution of one random variable which is continuous and another random variable which is discrete arises when one wishes to use a logistic regression in predicting the probability of a binary outcome Y conditional on the value of a continuously distributed outcome X. One *must* use the "mixed" joint density when finding the cumulative distribution of this binary outcome because the input variables (*X*, *Y*) were initially defined in such a way that one could not collectively assign it either a probability density function or a probability mass function. Formally, *f*_{X,Y}(*x*, *y*) is the probability density function of (*X*, *Y*) with respect to the product measure on the respective supports of *X* and *Y*. Either of these two decompositions can then be used to recover the joint cumulative distribution function:

- $$

\begin{align} F_{X,Y}(x,y)&=\sum\limits_{t\le y}\int_{s=-\infty}^x f_{X,Y}(s,t)\;ds \end{align} The definition generalizes to a mixture of arbitrary numbers of discrete and continuous random variables.

## Joint distribution for independent variables

If for discrete random variables $\backslash \; P(X\; =\; x\; \backslash \; \backslash mbox\{and\}\; \backslash \; Y\; =\; y\; )\; =\; P(\; X\; =\; x)\; \backslash cdot\; P(\; Y\; =\; y)$ for all *x* and *y*, or for absolutely continuous random variables $\backslash \; f\_\{X,Y\}(x,y)\; =\; f\_X(x)\; \backslash cdot\; f\_Y(y)$ for all *x* and *y*, then *X* and *Y* are said to be independent. This means that acquiring any information about the value of one or more of the random variables leads to a conditional distribution of any other variable that is identical to its unconditional (marginal) distribution; thus no variable provides any information about any other variable.

## Joint distribution for conditionally dependent variables

If a subset $A$ of the variables $X\_1,\backslash cdots,X\_n$ is conditionally dependent given another subset $B$ of these variables, then the joint distribution $\backslash mathrm\{P\}(X\_1,\backslash ldots,X\_n)$ is equal to $P(B)\backslash cdot\; P(A\backslash mid\; B)$. Therefore, it can be efficiently represented by the lower-dimensional probability distributions $P(B)$ and $P(A\backslash mid\; B)$. Such conditional independence relations can be represented with a Bayesian network.

## See also

- Chow–Liu tree
- Conditional probability
- Copula (statistics)
- Disintegration theorem
- Multivariate statistics
- Statistical interference

## External links

- Mathworld: Joint Distribution Function

**
**