Random Sampling Reduction

Draw a random real number r 1 r_1 between 0 and 10.
Repeat, draw a random number r 2 r_2 between 0 and r 1 r_1 .
Continue in this manner until the number drawn is below 1.

What is the expected value of drawings in this game (to 2 decimal places)?

Bonus: Generalize the answer.


The answer is 3.30.

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2 solutions

Rohith M.Athreya
Feb 21, 2017

I did it using pretty elementary math,but thought it might be worth a mention

Probability of finishing in 1 turn

1 10 \large \displaystyle \frac{1}{10}

Probability of finishing in 2 turns

1 10 d r 1 10 × 1 r 1 = l n 10 10 \large \displaystyle \int_{1}^{10}\frac{dr_{1}}{10} \times \frac{1}{r_{1}}=\frac{ln10}{10}

Probability of finishing in 3 turns

1 10 1 10 1 r 1 d r 2 r 1 r 2 d r 1 = 1 10 ( l n 10 ) 2 2 ! \large \displaystyle \frac{1}{10} \int_{1}^{10} \int_{1}^{r_{1}}\frac{dr_{2}}{r_{1}r_{2}}dr_{1}=\frac{1}{10} \frac{(ln10)^{2}}{2!}

Probability of finishing in n n turns (By induction )

1 10 1 10 1 r 1 1 r 2 . . . . 1 r n 2 d r n 1 r 1 r 2 r 3 . . . r n 1 d r n 2 d r n 3 . . . d r 1 = 1 10 ( l n 10 ) n 1 ( n 1 ) ! \large \displaystyle \frac{1}{10} \int_{1}^{10} \int_{1}^{r_{1}} \int_{1}^{r_{2}}.... \int_{1}^{r_{n-2}}\frac{dr_{n-1}}{r_{1}r_{2}r_{3}...r_{n-1}}dr_{n-2}dr_{n-3}...dr_{1}=\frac{1}{10} \frac{(ln10)^{n-1}}{(n-1)!}


Expected value of number of turns

1 10 r = 0 ( r + 1 ) x r r ! \large \displaystyle \frac{1}{10} \sum_{r=0}^{\infty}\frac{(r+1)x^{r}}{r!} where, x = l n 10 x=ln10

1 10 r = 0 ( r + 1 ) x r r ! = 1 10 ( e x + x e x ) = 1 + l n 10 3.303 \large \displaystyle \frac{1}{10} \sum_{r=0}^{\infty}\frac{(r+1)x^{r}}{r!}=\frac{1}{10}(e^{x}+xe^{x})=1+ln10 \approx 3.303

This is the simplest approach!

For clarity, you should show that this works for all positive integers n n via induction

Pi Han Goh - 4 years, 3 months ago

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yes indeed. i have added that!

Rohith M.Athreya - 4 years, 3 months ago

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LOL, that's not how to do that! Unless you're being lazy, you can't just go "oh, this works by induction" because that doesn't always work.

For clarity, you should work on the base hypothesis and inductive hypothesis.

On the other hand, we can solve this problem using the one-line of Order Statistic .

Pi Han Goh - 4 years, 3 months ago
Brian Moehring
Feb 20, 2017

Let N x N_x denote the number of turns in the slightly more general game where r 1 r_1 is chosen between 0 0 and x x and let F ( x ) = E [ N x ] F(x) = \mathbb{E}[N_x] .

Then we immediately have the conditional expectation E [ N x r 1 ] = { 0 x < 1 1 + F ( r 1 ) x 1 \mathbb{E}[N_x|r_1] = \begin{cases}0 & x<1 \\ 1 + F(r_1) \quad & x\geq 1\end{cases} and therefore F ( x ) = E [ E [ N x r 1 ] ] = { 0 x < 1 1 + 0 x 1 x F ( r 1 ) d r 1 x 1 = { 0 x < 1 1 + 1 x 1 x F ( r 1 ) d r 1 x 1 \begin{aligned} F(x) = \mathbb{E}\Big[\mathbb{E}[N_x|r_1]\Big] &= \begin{cases}0 & x<1 \\ 1 + \int_0^x \frac{1}{x}F(r_1)\,dr_1 \quad & x \geq 1\end{cases} \\ &= \begin{cases}0 & x<1 \\ 1 + \frac{1}{x}\int_1^x F(r_1)\,dr_1 \quad & x \geq 1\end{cases} \end{aligned}

For the rest of the problem, we focus purely on the case where x 1 x \geq 1 , under which we have just shown F ( x ) = 1 + 1 x 1 x F ( t ) d t . F(x) = 1 + \frac{1}{x}\int_1^x F(t)\,dt.

By rearranging, x ( F ( x ) 1 ) = 1 x F ( t ) d t , x\big(F(x) - 1\big) = \int_1^x F(t)\, dt, and differentiating, F ( x ) 1 + x F ( x ) = F ( x ) , F(x) - 1 + xF'(x) = F(x), we can write F ( x ) = 1 x F ( x ) = C + ln x . F'(x) = \frac{1}{x} \Rightarrow F(x) = C + \ln x.

Looking back at the integral equation for F ( x ) F(x) when x 1 x\geq 1 , we see that F ( 1 ) = 1 F(1) = 1 , so we may solve for C C to find C = 1 C=1 . Therefore, we have F ( x ) = { 0 x < 1 1 + ln x x 1 F(x) = \begin{cases}0 & x < 1 \\ 1 + \ln x \quad & x\geq 1\end{cases} so the expected number of moves when x = 10 x=10 is F ( 10 ) = 1 + ln 10 3.30 F(10) = 1 + \ln 10 \approx \boxed{3.30} .

Note: There's a sizable hole in the above proof - see if you can find it! All of the results are true, but it would require a little bit more work to be a formal proof.

Great! The generalized version makes the problem much easier to deal with. Very similar to the Feynman trick of differentiating through the integral.

Calvin Lin Staff - 4 years, 3 months ago

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