# How to define a 2D Gaussian using 1D variance of component Gaussians

I am trying to define a 2 dimensional Gaussian in which each dimension has a different variance.

One way I thought of was just to define it as the product of 1D Gaussians, but I wasn't sure if that would hold up. Numerically plotting the product of two zero-mean, unit-variance distributions appears to give the concentric circles that one would expect in the 2D case. Furthermore, adjusting the variance of one warps the plot to concentric ellipses. However, I want to be able to express it mathematically, not numerically. Mathematically expressed, the product of two 1D Gaussians doesn't match up with the expression I'm used to seeing for a multivariable Gaussian distribution.

Multivariate Gaussian where $x \in \mathbb{R}^2$ :

$$\frac{1}{\sqrt{(2\pi)^{k}|\Sigma|}} \exp \left(-\frac{1}{2}(x - \mu)^{T}\Sigma^{-1}(x - \mu) \right)$$

Product of two 1D Gaussians:

$$\frac{1}{\sigma_{1}\sigma_{2}2\pi} \exp \left( -\frac{(x_1 - \mu_1)^2}{2\sigma_1^2} -\frac{(x_2 - \mu_2)^2}{2\sigma_2^2} \right)$$

Where am I going wrong here? Would setting $\Sigma$ in the multivariate gaussian to diag($\sigma_1$, $\sigma_2$) do the trick?

• Your expression for the multivariate gaussian is incorrect: on either side of $\Sigma^{-1}$ you should see the vectors $(x_1-\mu_1, x_2-\mu_2)$. When you make that change it should be obvious how to express $\Sigma$ in terms of $\sigma_1$ and $\sigma_2$.
– whuber
Feb 24, 2016 at 20:16
• If I understand your questions right, then you are trying to have the math line up so that the product of 2 independent Gaussians is a bivariate Gaussian. This is not true. math.stackexchange.com/questions/101062/… Feb 24, 2016 at 20:21
• Your expression for the 2-variate normal is still wrong. The term after $\Sigma^{-1}$ should also be $(x_1 - \mu_1)$. There is no need for $x_2$ and $\mu_2$. Both $x_1$ and $\mu_1$ are vectors. Feb 24, 2016 at 20:25
• @Greenparker I think you may be confusing the issue by conflating the product of random variables (which is not of concern in this question) with the product of pdfs.
– whuber
Feb 24, 2016 at 20:29
• Ah, I see now. Thanks for clarifying @whuber. You had the right idea at the end. I have added a solution. Feb 24, 2016 at 20:43

Let $$x \sim N_2(\mu, \Sigma)$$ where $$\Sigma = \left [\begin{array}{cc} \sigma_1^2 & 0\\ 0 & \sigma_2^2 \end{array} \right]$$. Then det($$\Sigma)$$ = $$\sigma_1^2 \sigma_2^2$$
\begin{align*} & \dfrac{1}{\sqrt{(2\pi)^2 \mathrm{det(}\Sigma)}} \exp\left \{ -\dfrac{1}{2}(x - \mu)^T \Sigma^{-1} (x - \mu) \right \}\\ = & \dfrac{1}{2\pi \sigma_1 \sigma_2} \exp \left \{-\dfrac{1}{2} \left[x_1 - \mu_1\quad x_2 - \mu_2 \right]\left [\begin{array}{cc} 1/\sigma_1^2 & 0\\ 0 & 1/\sigma_2^2 \end{array} \right] \left[ \begin{array}{c} x_1 - \mu_1\\ x_2 - \mu_2 \end{array} \right] \right \}\\ = & \dfrac{1}{2\pi \sigma_1 \sigma_2} \exp \left \{-\dfrac{1}{2} \left[\dfrac{x_1 - \mu_1}{\sigma^2_1} \quad \dfrac{x_2 - \mu_2}{\sigma^2_2} \right] \left[ \begin{array}{c} x_1 - \mu_1\\ x_2 - \mu_2 \end{array} \right] \right \}\\ = &\dfrac{1}{2\pi \sigma_1 \sigma_2} \exp \left \{ -\dfrac{(x_1 - \mu_1)^2}{2\sigma_1^2} - \dfrac{(x_2 - \mu_2)^2}{2 \sigma_2^2} \right \}. \end{align*}
• +1. But the first line of the formula may need a little fixing up (in the denominator) to match the correct general formula. The algebra goes $$\sqrt{(2\pi)^k|\Sigma|} = \sqrt{(2\pi)^2 \sigma_1^2\sigma_2^2} = 2\pi\sigma_1\sigma_2$$ (assuming the $\sigma_i$ are positive).