The characteristic function of a random variable $X$ is defined as $\hat{X}(\theta)=\mathbb{E}(e^{i\theta X})$. If $X$ is a normally distributed random variable with mean $\mu$ and standard deviation $\sigma\ge 0$, then its characteristic function can be found as follows:

$$\hat{X}(\theta)=\mathbb{E}(e^{i\theta X}) =\int_{-\infty}^{\infty}\frac{e^{i\theta x-\frac{(x-\mu)^2}{2\sigma^2}}}{\sigma\sqrt{2\pi}}dx=\ldots=e^{i\mu\theta-\frac{\sigma^2\theta^2}{2}}$$

(to be honest, I have no idea what to put instead of the "$\ldots$"; I've looked here, but that's only for the standard case. Anyway, this is not really my question, even if it is interesting and might be relevant)

Now, if I got it right, a random Gaussian vector $X$ (of dimension $n$) is a vector of the form $X=AY+M$ where $A$ is any real square matrix $n\times n$, $Y$ is a vector of size $n$ in which each coordination is a standard normally distributed random variable, and $M$ is some (constant) vector of size $n$.

I am trying to find the characteristic function of such $X$. The generalization of the formula for characteristic functions to higher dimensions is straight-forward:

$$\hat{X}=\mathbb{E}(e^{i<\theta,X>}),$$ where $<.,.>$ here is an inner product. So I can start with the following:

$$\hat{X}(\theta) = \mathbb{E}(e^{i<\theta,X>}) = \mathbb{E}(e^{i<\theta,AY>}\cdot e^{i<\theta,M>})\\ =e^{i<\theta,M>}\cdot \mathbb{E}(e^{i<\theta,AY>}) $$

And I'm left with an expectation of a complex product of random variables. That probably means that the covariance matrix of some random variables should be involved, but that touches the boundaries of my knowledge about probability.

  • $\begingroup$ $\theta$ - what is that? $\endgroup$
    – dtn
    May 15, 2023 at 14:47

2 Answers 2


You wouldn't want to use the bracket notation for inner product when you're essentially dealing with matrices. Instead, write $\mathbb{E}\left[e^{i\theta^{T}X}\right] = \mathbb{E}\left[e^{i\theta^{T}\left(AY+M\right)}\right] = e^{i\theta^{T}M}\mathbb{E}\left[e^{i\theta^{T}AY}\right]$. You're only left with computing the characteristic function of a multivariate Gaussian distribution. $$ \begin{align*}X &\sim \mathcal{N}\left(\mu, \Sigma\right)\\ \mathbb{E}\left[e^{is^{T}X}\right] &= \exp \left\{i\mu^{T}s - \frac{1}{2}s^{T}\Sigma s \right\} \end{align*} $$ Just find out the mean vector and the covariance matrix of $AY$ since Gaussian variables have the affine property which means they don't change under linear transformation (They're still Gaussian completely defined by the mean vector and covariance matrix). If $Y \sim \mathcal{N}\left(\mu_{Y}, \Sigma_{Y}\right)$, then $$ \begin{align*} \mathbb{E}\left[AY\right] &= A\mu_{Y} \\ \operatorname{Var}\left[AY\right] &= A\Sigma_{Y} A^{T} . \end{align*} $$

Using the relationship between $X$ and $Y$, $$ \begin{align*} AY &= X-M \\ \mathbb{E}\left[AY\right] &= \mu_{X} - M \\\operatorname{Var}\left[AY\right] &= \Sigma_{X}\\ \mathbb{E}\left[e^{i\theta^{T}AY}\right] &= \exp \left\{i\left(\mu_{X}-M\right)^{T}\theta - \frac{1}{2}\theta^{T}\Sigma_{X} \theta \right\} . \end{align*} $$ This is as far as I can get with the information you gave.

  • $\begingroup$ what added value, in terms of statistical inference, does calculating the characteristic function of a multivariate Gaussian have over just analyzing the multivariate Gaussian itself? $\endgroup$
    – develarist
    Dec 5, 2020 at 3:02
  • $\begingroup$ @develarist “multivariate Gaussian itself” cannot be analyzed unless you get a tangible handle on it whether it be the density, cdf, mgf, or the characteristic function. CF is useful because it always exists e.g., you can’t prove an mgf converges to that of a normal unless the mgf exists. You don’t have this constraint for CF. $\endgroup$
    – Daeyoung
    Dec 7, 2020 at 3:14
  • $\begingroup$ Besides the existence property, what inferences about the variable can be derived from knowing its characteristic function is my question $\endgroup$
    – develarist
    Dec 7, 2020 at 3:24
  • $\begingroup$ @develarist I don't claim expertise in this area but I have seen limited research in that regard. CF makes an appearance in GMM literature every so often (example here) but other than that it's hardly used for inference. Inference requires information about the sampling process, which CF doesn't expressly describe. CF is, for this reason, more valuable when investigating the probabilistic properties of a distribution. $\endgroup$
    – Daeyoung
    Dec 7, 2020 at 3:47
  • $\begingroup$ If anybody is feeling confused, it is in my opinion, that a better answer is math.stackexchange.com/questions/3356061/…. $\endgroup$ May 14 at 15:10

You are basically finished! See, you obtained $$ \Psi_X(\theta) = e^{(i\langle\theta,M\rangle)}\mathbb{E}(e^{(i\langle\theta,AY\rangle)}) $$ What is left is noticing that A can move to the other side of the inner product $$ = e^{(i\langle\theta,M\rangle)}\mathbb{E}(e^{(i\langle A'\theta,Y\rangle)}) = e^{(i\langle\theta,M\rangle)}\Psi_Y(A'\theta) $$ All you have left is plugging in the characteristic function of the multivariate normal distribution.


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