# joint normal distribution

 $\lambda_{1}X_{1}+\lambda_{2}X_{2}+\cdots+\lambda_{n}X_{n}$

are normal (http://planetmath.org/NormalRandomVariable). This implies, in particular, that the individual random variables $X_{i}$ are each normally distributed. However, the converse  is not not true and sets of normally distributed random variables need not, in general, be jointly normal.

If $\boldsymbol{X}=(X_{1},X_{2},\ldots,X_{n})$ is joint normal, then its probability distribution is uniquely determined by the means $\boldsymbol{\mu}\in\mathbb{R}^{n}$ and the $n\times n$ positive semidefinite covariance matrix  $\boldsymbol{\Sigma}$,

 $\displaystyle\mu_{i}=\mathbb{E}[X_{i}],$ $\displaystyle\Sigma_{ij}=\operatorname{Cov}(X_{i},X_{j})=\mathbb{E}[X_{i}X_{j}% ]-\mathbb{E}[X_{i}]\mathbb{E}[X_{j}].$

Then, the joint normal distribution is commonly denoted as $\operatorname{N}(\boldsymbol{\mu},\boldsymbol{\Sigma})$. Conversely, this distribution   exists for any such $\boldsymbol{\mu}$ and $\boldsymbol{\Sigma}$. Figure 1: Density of joint normal variables X,Y with Var⁡(X)=2, Var⁡(Y)=1 and Cov⁡(X,Y)=-1.

The joint normal distribution has the following properties:

1. 1.

If $\boldsymbol{X}$ has the $\operatorname{N}(\boldsymbol{\mu},\boldsymbol{\Sigma})$ distribution for nonsigular $\boldsymbol{\Sigma}$ then it has the multidimensional Gaussian probability density function

 $f_{\boldsymbol{X}}(\boldsymbol{x})=\frac{1}{\sqrt{(2\pi)^{n}\det{\boldsymbol{(% \Sigma})}}}\exp\left(-\frac{1}{2}(\boldsymbol{x}-\boldsymbol{\mu})^{% \operatorname{T}}\boldsymbol{\Sigma}^{-1}(\boldsymbol{x}-\boldsymbol{\mu})% \right).$
2. 2.

If $\boldsymbol{X}$ has the $\operatorname{N}(\boldsymbol{\mu},\boldsymbol{\Sigma})$ distribution and $\boldsymbol{\lambda}\in\mathbb{R}^{n}$ then

 $\boldsymbol{\lambda}\cdot\boldsymbol{X}=\lambda_{1}X_{1}+\cdots+\lambda_{n}X_{% n}\sim\operatorname{N}(\boldsymbol{\lambda}\cdot\boldsymbol{\mu},\boldsymbol{% \lambda}^{\operatorname{T}}\boldsymbol{\Sigma}\boldsymbol{\lambda}).$
3. 3.

Sets of linear combinations of joint normals are themselves joint normal. In particular, if $\boldsymbol{X}\sim\operatorname{N}(\boldsymbol{\mu},\boldsymbol{\Sigma})$ and $A$ is an $m\times n$ matrix, then $A\boldsymbol{X}$ has the joint normal distribution $\operatorname{N}(A\boldsymbol{\mu},A\boldsymbol{\Sigma}A^{\operatorname{T}})$.

4. 4.
 $\varphi_{\boldsymbol{X}}(\boldsymbol{a})\equiv\mathbb{E}\left[\exp(i% \boldsymbol{a}\cdot\boldsymbol{X})\right]=\exp\left(i\boldsymbol{a}\cdot% \boldsymbol{\mu}-\frac{1}{2}\boldsymbol{a}^{\operatorname{T}}\boldsymbol{% \Sigma}\boldsymbol{a}\right),$

for $\boldsymbol{X}\sim\operatorname{N}(\boldsymbol{\mu},\boldsymbol{\Sigma})$ and any $\boldsymbol{a}\in\mathbb{C}^{n}$.

5. 5.
6. 6.

Let $\boldsymbol{X}$ be a random vector whose distribution is jointly normal. Suppose the coordinates  of $\boldsymbol{X}$ are partitioned into two groups, forming random vectors $\boldsymbol{X_{1}}$ and $\boldsymbol{X_{2}}$, then the conditional distribution of $\boldsymbol{X_{1}}$ given $\boldsymbol{X_{2}}=\boldsymbol{c}$ is jointly normal.

Title joint normal distribution JointNormalDistribution 2013-03-22 15:22:34 2013-03-22 15:22:34 gel (22282) gel (22282) 14 gel (22282) Definition msc 62H05 msc 60E05 multivariate Gaussian distribution NormalRandomVariable jointly normal multivariate normal distribution