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Assume that $U$ and $V$ are independent normally distributed random variables, each with mean $0$ and variance $1$.

Find the probability that the roots of the polynomial $P(x) = \frac{1}{4} x^2 +Ux+V^2$ are real.

This is a question I had to work out on my intro probability midterm. It's clear to see by looking at the discriminant that $$\begin{align} \Bbb P(\text{roots of }P(x)\text{ are real}) &=\Bbb P(U^2-V^2\ge0) \\ &=\Bbb P(U^2\ge V^2) \\ &=\Bbb P(U\ge V\text{ or }-U\ge -V), \end{align}$$ but what next?

I appreciate any help.

Thanks in advance!

Brian M. Scott
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Galois
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1 Answers1

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Since the variables are independent and Normally distributed, the codensity function of $u$ and $v$ is very symmetric about $(0,0)$ in the $(u,v)$-plane. You are looking for $P(|U|\geq |V|)$. Sketching the region "$|U|\geq |V|$" in the $(u,v)$-plane, we have two sectors that are each one-quarter of the plane. All the symmetry granted in the setup allows us to conclude that the probability is $\frac12$.

2'5 9'2
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  • Why not following @achille.hui's suggestion instead of delving into irrelevant specifics of the distribution? The couples (U,V) and (V,U) are identically distributed and [U=V] has probability zero, QED. – Did Jul 14 '13 at 08:31
  • @Did Is it not more relevant that $[|U|=|V|]$ has measure $0$? OP does not want a region containing, say $u=1,v=-2$, even though $U>V$ there. Also I intended to appeal to symmetry; I though I was avoiding details, only giving enough details to justify the symmetry. Thanks for the second glance though- I had some incorrect inequalities. – 2'5 9'2 Jul 15 '13 at 07:19
  • The new version is better although, oddly enough, neither the argument guaranteeing a symmetry nor the exact symmetry involved are mentioned. Since one could write down a full solution without even using the word "normal", I am not sure (once again) that to rely on the specifics of the standard gaussian distribution yields the best possible answer. – Did Jul 15 '13 at 07:58