One of my friends recently posed the problem of finding the expected number of uniform random variables needed to get a sum greater than 1 without using calculus.
Even though I've seen this problem before, I was not able to recollect it and the calculus proof was all I could come up with until he eventually showed the me the solution. But this time, something different struck me.
The solution to this problem uses that fact that
\displaystyle \mathbb{P}(N>n)=\frac{1}{n!}
where N is the random variable indicating the number of Uniform random variables needed. But given this, we could do more than the expected value. We know that,
\displaystyle \mathbb{E}(N)=\sum_{n=0}^\infty\mathbb{P}(N>n)=\sum_{n=0}^\infty \frac{1}{n!}=e
which follows from the definition of Euler's number.
But suppose, we don't know the exact value of e. With elementary methods, we can show that 2<e<3.
In the problem that we were discussing, we can use the expression for probability to find \mathbb{V}(N) as well. We know that
\displaystyle \mathbb{E}(N^2)=\sum_{n=0}^\infty(2n+1)\mathbb{P}(N>n)=\sum_{n=0}^\infty \frac{2n+1}{n!}=3e
Therefore, \mathbb{V}(N)=\mathbb{E}(N^2)-\mathbb{E}(n)=e(e-3)
As variance of any random variable \geq 0 and we know that e>0, this shows us that e<3 which is something we already know. But this gives us a nice idea.
Let N_{(2)} be the random variable denoting the number of Uniform random variables needed to get a sum a sum greater than 2. Uniform Sum Distribution shows us that,
\displaystyle \mathbb{P}(N_{(2)}=n)=\frac{(n-2)(2^{n-1}-n)}{n!}
With this, we can see that
\displaystyle \mathbb{E}(N_{(2)})=e(e-1) and \displaystyle \mathbb{E}(N_{(2)}^2)=5e(e-1)
Therefore, \displaystyle \mathbb{V}(N_{(2)})=e(e-1)(5-e^2+e)
Using the same idea as before, we see that 5-e^2+e>0 which shows us
\displaystyle \frac{1}{2}(1-\sqrt{21}) \leq e \leq \frac{1}{2}(1+\sqrt{21})
Numerically, this shows that e \leq 2.791288!!
As you may have already guessed, this could be extended for higher moments and / or sum-greater-than-3 and so on. But the resulting expressions gives higher degree polynomials which, even though gives better estimates for e, cannot be easily solved in closed form. For example, using
\displaystyle \mathbb{E}(N_{(3)})=\frac{1}{2}(2e^3-4e^2+e) and \displaystyle \mathbb{E}(N_{(3)}^2)=\frac{7}{2}(2e^3-4e^2+e)
Solving the resulting expression for variance, we get to see that 2e^3-4e^2+e-14 \leq 0. Solving this with WA, we see that
\displaystyle e < \frac{1}{6}(4+\sqrt[3]{784+18\sqrt{1894}}+\sqrt[3]{784-18\sqrt{1894}}) \approx 2.74615
Hope you enjoyed this. See ya soon.
Until then, Yours Aye
Me
No comments:
Post a Comment