# Proof that any plane perpendicular to x,y plane intersection with the bivariate normal distribution has the shape of a normal distribution.

The bivariate normal distribution is given by the equation:

$$f(x,y)=\frac{\exp\left(-\frac{1}{2(1-\rho)^2}\left[\left(\frac{x-\mu_1}{\sigma_1}\right)^2-2\rho\left(\frac{x-\mu_1}{\sigma_1}\right)\left(\frac{y-\mu_2}{\sigma_2}\right)+\left(\frac{y-\mu_2}{\sigma_2}\right)^2\right]\right)}{2\pi\sigma_1\sigma_2\sqrt{1-\rho^2}}$$

for $-\infty<x<\infty$ and $-\infty<y<\infty$, where $\sigma_1>0$, $\sigma_2>0$, and $-1<p<1$.

The normal distribution is given by the equation:

$$f(x)=\frac{e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^2}}{\sigma\sqrt{2\pi}}$$

for $-\infty<x<\infty$ where $\sigma>0$.

I am trying to show that if I take any plane that is perpendicular to the $x,y$ plane, it's intersection with the bivariate normal distribution is equivlent to $c\cdot f(x)$, where $f(x)$ is the normal distribution and $c$ is a real constant.

This can also be written as showing the following statement to be true.

$$\frac{\exp\left(-\frac{1}{2(1-\rho)^2}\left[\left(\frac{x-\mu_1}{\sigma_1}\right)^2-2\rho\left(\frac{x-\mu_1}{\sigma_1}\right)\left(\frac{(mx+b)-\mu_2}{\sigma_2}\right)+\left(\frac{(mx+b)-\mu_2}{\sigma_2}\right)^2\right]\right)}{2\pi\sigma_1\sigma_2\sqrt{1-\rho^2}}=c\cdot \frac{e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^2}}{\sigma\sqrt{2\pi}}$$ or $$f(x,mx+b)=c\cdot f(x)$$ Where $m$ and $b$ are any real number.

This does leave out all the planes parallel to the $x$ axis, although those will be trivial to show separately, after showing that $f(y,x)$ is also a bivariate normal distribution.

I tried simplifying this but have made no progress so far, It's quite likely that I am going about this in completely the wrong way.

• When you write "$xy$ axis", do you mean the $x,y$ plane (the set of all points of the form $(x,y,0)$)? And when you say the intersection of a plane with a bivariate distribution, you mean the intersection with the three-dimensional graph of a bivariate distribution, the set of points of the form $(x,y,f(x,y))$? – David K Dec 11 '16 at 21:56
• Anyway your basic idea looks like something I would try. I might transform the function a bit first to simplify it; for example, translation by $(-\mu_1,-\mu_2)$ to map it to a function whose means are zero. – David K Dec 11 '16 at 22:03
• I took the liberty of using \left( and \right( to make the size of your parentheses match the formulas they contain, and \cdot instead of $*$ as a multiplication symbol. You can roll these changes back if you don't approve. I would suggest, however, to use the function notation \exp(x), which means $e^x$ but is much easier to read when the exponent is a very complicated formula. – David K Dec 11 '16 at 22:17
• @davidK Yes, I did mean $x,y$ plane, that has been edited, and yes it would be the set of all points $(x, y, 0)$. When I say intersection between a plane and a bivariate distribution, then yes again I mean all points in the form $(x, y, f(x,y)$. I also want to thank you as I did not know about \cdot or \exp. – Benji Altman Dec 12 '16 at 1:30

The intersection of the plane with coordinate axes $x$ and $y$ (hereinafter "the $x$-$y$ plane") with a plane that is perpendicular to the $x$-$y$ plane is a straight line $ax+b =c$ where at least one of $a$ and $b$ is nonzero. So, what you are are being asked to show is that when you constrain $x$ and $y$ in $f_{X,Y}(x,y)$ to satisfy $ax+by = c$, what you get is a multiple of a normal density function.

So, assuming that $b \neq 0$, replace $y$ by $\frac{c-ax}{b}$ in $$\frac{1}{2(1-\rho)^2}\left[\left(\frac{x-\mu_1}{\sigma_1}\right)^2-2\rho\left(\frac{x-\mu_1}{\sigma_1}\right)\left(\frac{y-\mu_2}{\sigma_2}\right)+\left(\frac{y-\mu_2}{\sigma_2}\right)^2\right].$$ It should be obvious (but if not, just do a lot of middle-school level algebra) that what is left is a quadratic function of $x$, viz. $\alpha x^2 + \beta x + \gamma$. What is not quite so obvious is that $\alpha$ is a positive number. Note that \begin{align} \alpha &= \frac{1}{2(1-\rho^2)}\left[\frac{1}{\sigma_1^2} + 2\rho\frac{a}{b\sigma_1\sigma_2} + \frac{a^2}{b^2\sigma_2^2}\right]\\ &= \frac{1}{2(1-\rho^2)}\left[\left(\frac{a}{b\sigma_2}+\frac{\rho}{\sigma_1}\right)^2 + \left.\left.\frac{1}{\sigma_1^2} \right(1-\rho^2\right)\right]\\ &> 0 \end{align} since $1-\rho^2 > 0$. Consequently, $\alpha x^2 + \beta x + \gamma$ can be massaged (via the technique known as "completing the square" which is illustrated above) into $$\frac{1}{2}\left(\frac{x-\mu_3}{\sigma_3}\right)^2 + \delta$$ for $\mu_3$, $\sigma_3$ and $\delta$ which can be written in terms of $a,b,c$ and the parameters of the bivariate distribution (the expressions are messy). And so we are done; the bivariate normal pdf has been shown to be reduced to a univariate normal pdf times a constant. Note that it $b = 0$ (and so $a \neq 0$), we can substitute for $x$ in terms of $y$ and get a univariate normal pdf with argument $y$ instead of $x$.

Think of the bivariate normal pdf as defining a solid with base the $x$-$y$ plane and upper surface $z = f_{X,Y}(x,y)$. Then what all this is saying is that every cross-section of this solid (by a plane perpendicular to the $x$-$y$ plane) has the shape of a normal pdf (except that the "area under the curve" is not necessarily equal to $1$ as all valid pdfs must have). A fanciful way, familiar to Americans, of putting it is that the solid is a piece of bologna: no matter how you slice it, it is still bologna!

• This seems like a straightforward approach. The only thing I would add would be to verify that $\alpha>0$; otherwise, I think completing the square would not get the result shown. (The sign of the square term would be wrong.) – David K Dec 12 '16 at 4:41
• @DavidK I have added material to show that your fears are groundless; the method works even when $a < 0$. – Dilip Sarwate Dec 12 '16 at 18:00
• It was obvious to me all along that $\alpha$ is positive, since I know (for other reasons) that the theorem is true. But simply changing the sign of $\rho$ whenever its existing sign is found to be inconvenient seems like a questionable move to me. Instead, one can proceed as I do in my answer. – David K Dec 12 '16 at 18:39
• @DavidK My apologies. My previous edit was incorrect, and I have included a demonstration of the fact that $\alpha$ is always positive. Thanks for persisting in questioning my previous idea as a dubious one. – Dilip Sarwate Dec 13 '16 at 16:45
• Nice work. I see I have already upvoted the answer. It seems to me this is what OP needs, I hope they agree. – David K Dec 13 '16 at 17:09

As you can see from another answer, it is possible to carry through your initial idea to get a proof.

The one detail that I questioned was how we establish that the coefficient of $x^2$ in $$\frac{1}{(1-\rho)^2}\left[\left(\frac{x-\mu_1}{\sigma_1}\right)^2-2\rho\left(\frac{x-\mu_1}{\sigma_1}\right)\left(\frac{mx+b-\mu_2}{\sigma_2}\right)+\left(\frac{mx+b-\mu_2}{\sigma_2}\right)^2\right]$$ is positive (using $m$ and $b$ as defined in the question). In fact, that coefficient is $$\alpha = \frac{1}{(1-\rho)^2} \left(\frac{1}{\sigma_1^2}-2\rho\frac{m}{\sigma_1\sigma_2}+\frac{m^2}{\sigma_2^2}\right) .$$ But $$\frac{1}{\sigma_1^2}-2\rho\frac{m}{\sigma_1\sigma_2}+\frac{m^2}{\sigma_2^2} = \left(\frac{1}{\sigma_1}-\rho\frac{m}{\sigma_2}\right)^2 + (1 - \rho^2)\frac{m^2}{\sigma_2^2},$$ and since $-1<\rho<1$ it follows that $1-\rho^2>0$ and also that $\alpha > 0.$ Note that for any $\rho$ such that $\lvert\rho\rvert>1$ it is possible to choose the other parameters so that $\alpha<0,$ and of course the entire polynomial is undefined for $\lvert\rho\rvert=1,$ so the fact that $-1<\rho<1$ is a necessary condition for this proof to go through.

An alternative is a suitable substitution of variables for both $x$ and $y$ that transform the plane so that the distribution over the new variables is standard normal with covariance zero over the transformed variables and such that the equation of the line in the transformed variables has the form $y'=k.$ Such a substitution certainly exists, but it might involve translation (to eliminate the means), rotation (to eliminate the covariance), scaling by unequal factors along the transformed axes (to eliminate the variances), and then a final rotation to make the perpendicular plane in the question intersect the transformed coordinate plane in a "horizontal" line. This method is conceptually simple, and avoids the concern about signs that I had with the other approach, but you may find that actually working through the math of the other approach is easier for you.

This is one way of doing it with a geometric flavour, whatever your original Gaussian is you can carry out a linear transformation to a new basis given by the eigenvectors of the covariance matrix so that the elliptical contours of our Gaussian become circles, lets call the new coordinate system $(x,y)$, are of the form $$x^2 + y^2 = c,$$ your original line will also be transformed to a new line, which again to keep the notation simple I will just say is given by $y = mx + d$, now using Lagrangian multiplier or whatever you can find the circle tangent to this line, say the point $\hat{x},\hat{y}$, so we then represent the line in vector form in terms of the single variable \begin{align} \ell(t) &= \hat{p} + td \\ &=\begin{bmatrix} \hat{x} \\ \hat{y} \end{bmatrix} + t \begin{bmatrix} 1 \\ m \end{bmatrix}, \end{align} so defining the function $f(t) = F(x(t),y(t))$ we have \begin{align} f^{\prime}(t) &= \nabla F \cdot d \\ &= -\left[ x(t) + m\cdot y(t)\right]f(t) \end{align} now I think with a little bit of tidying up you could show this satisfies the same ODE as the univariate Gaussian does, i.e. $\sigma^2 f^{\prime}(z) + (z-\mu)f(z) = 0.$ However going down a slightly different track we see that \begin{align} \frac{d^2}{dt^2} \log f(t) = -1-m^2, \end{align} telling us that the log of this function is quadratic in $t$ and in fact that \begin{align} f(t) &= \sqrt{2\pi}F(\hat{x},\hat{y})\frac{1}{\sqrt{2\pi}}e^{-\frac{(1+m^2)t^2}{2} } \\ &= \hat{c}\sqrt{2\pi (1+m^2)} \cdot \varphi(t), \end{align} where $\hat{c} = F(\hat{x},\hat{y})$, i.e. the value of contour of the circle the line is tangent to, and $\varphi$ is the density of a standard univariate Gaussian.

To make this fully general one could then transform back to the original plane, but this was the basic strategy

1. Find the ellipse that the line is tangent to.
2. Reparameterise the line in a variable $t$ such that $t=0$ corresponds to the tangent point
3. You can then easily perform calculus using the directional derivative to either show $f(t)$ satisfies a certain ODE or probably easier show that the log function is quadratic in $t$ and then form the Laplace approximation