Keep calm and practice archery!

Mark is practicing for his match against Robin Hood. He shoots at a target board which is a unit circle.

For each real value of x x between 0 0 and 1 1 , suppose that the probability that each shot of Mark's will be r \leq r from the bullseye is r x r^x . Let f ( x , k ) f(x,k) be the expected value of the minimum area of the circle, whose center is the bullseye, and contains his best n k \left \lfloor \frac{n}{k} \right \rfloor shots as n n \to \infty .

If

m even k = 2 f ( 2 m , k ) = a π b , \displaystyle \sum_{\text{m even}}{ \sum_{k = 2}^{\infty}{f\left(\frac{2}{m},k\right)}} = \frac{a \pi}{b},

where a a and b b are coprime positive integers, then give your answer as a + b a+b .


The answer is 7.

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2 solutions

Mark Hennings
Dec 10, 2016

Let R R be the distance from the centre of the N N th best shot out of N k Nk shots. Then R R has PDF g ( r ) = N k × ( N k 1 N 1 ) ( r x ) N 1 × x r x 1 × ( 1 r x ) N ( k 1 ) = x r x 1 × r x ( N 1 ) ( 1 r x ) N ( k 1 ) B ( N , N ( k 1 ) + 1 ) 0 r 1 g(r) \; = \; Nk \times {Nk-1 \choose N-1} (r^x)^{N-1} \times xr^{x-1} \times (1-r^x)^{N(k-1)} \; = \; \frac{x r^{x-1} \times r^{x(N-1)} (1-r^x)^{N(k-1)}}{B(N,N(k-1)+1)} \hspace{1cm} 0 \le r \le 1 and so f ( x , k ) f(x,k) is the limit of the integral π 0 1 r 2 g ( r ) d r = B ( N + 2 x , N ( k 1 ) + 1 ) B ( N , N ( k 1 ) + 1 ) π \pi \int_0^1 r^2 g(r)\,dr \; = \; \frac{B(N+\frac{2}{x},N(k-1)+1)}{B(N,N(k-1)+1)}\pi as N N \to \infty . When x = 2 m x = \tfrac{2}{m} , where m m is a positive integer, we deduce that f ( 2 m , k ) = π k m f\big(\tfrac{2}{m},k\big) \; =\; \frac{\pi}{k^m} and since m = 1 π k 2 m = π k 2 ( 1 k 2 ) = π k 2 1 = 1 2 π ( 1 k 1 1 k + 1 ) \sum_{m=1}^\infty \frac{\pi}{k^{2m}} \; = \; \frac{\pi}{k^2(1 - k^{-2})} \; = \; \frac{\pi}{k^2-1} \; = \; \tfrac12\pi\left(\frac{1}{k-1} - \frac{1}{k+1}\right) we deduce that m 1 m e v e n k 2 f ( 2 m , k ) = 1 2 π k = 2 ( 1 k 1 1 k + 1 ) = 3 4 π \sum_{m \ge 1 \atop m\, \mathrm{even}} \sum_{k \ge 2} f\big(\tfrac{2}{m},k\big) \; = \; \tfrac12\pi\sum_{k=2}^\infty\left(\frac{1}{k-1} - \frac{1}{k+1}\right) \; = \; \tfrac34\pi making the answer 7 \boxed{7} .

@Mark Hennings Can you check my solution and see if anything is wrong?

Why is it a Geometry problem? I don't see.

Kartik Sharma - 4 years, 6 months ago

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Two problems. One minor, one major. The coefficient ( n t ) {n\choose t} in the definition of the PDF is wrong. You don't just have to choose the t t largest, you also have to choose which of the remaining n t n-t is the next biggest. You should check to see that your PDF does not integrate to 1 1 .

This is not a major problem, since the way you calculate the expectation cancels this error out.

Much more important, in the very last identity of you calculation of the expectation, you replace n t n-t by t = n / k t=n/k twice. For example Γ ( n t ) \Gamma(n-t) becomes Γ ( n / k ) \Gamma(n/k) in the denominator. This has the effect of reversing everything so that your formula in the end is what we get if we include the best n / k n/k , as was stated in the original problem, and as I just corrected it.

We should go back to the previous version of the problem, which is consistent with my solution.

Mark Hennings - 4 years, 6 months ago

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How can I improve the solution? I don't understand why we have to choose which of the remaining ( n t ) (n-t) is the next biggest.

Kartik Sharma - 4 years, 6 months ago

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@Kartik Sharma The simplest way would be to remove ( n t ) {n \choose t} from your PDF definitions, and replace it by a constant k k . You could then either determine k k by checking that the PDF integrates to 1 1 , or else you could proceed with your expectation calculation, during which the constant k k will cancel out.

Or think of it this way. You need to choose which t t to exclude, and then you need to decide which of the remaining n t n-t will be the largest, giving the PDF ( n t ) × ( n t ) × ( r x ) n t 1 × x r x 1 × ( 1 r x ) t {n \choose t} \times (n-t) \times (r^x)^{n-t-1} \times xr^{x-1} \times (1-r^x)^t Note that the coefficient is then n ! t ! ( n t 1 ) ! = Γ ( n + 1 ) Γ ( t + 1 ) Γ ( n t ) = 1 B ( t + 1 , n t ) \frac{n!}{t! (n-t-1)!} \; = \; \frac{\Gamma(n+1)}{\Gamma(t+1)\Gamma(n-t)} \; = \; \frac{1}{B(t+1,n-t)} which is what is required.

Of course, you will need to rewrite to make it clear that you are either excluding the worst n n k n-\lfloor\tfrac{n}{k}\rfloor , or (equivalently) only including the smallest n k \lfloor \tfrac{n}{k}\rfloor . You will need to change what you mean by t t from what is currently in your proof.

Mark Hennings - 4 years, 6 months ago

Here is another reason why things must be wrong as they are stated currently.

At the moment, you ask to exclude the top k k th of the shots. As you increase k k , the proportion of shots that are included therefore increases, and therefore the expected area enclosing them will also increase. That expectation cannot therefore converge to 0 0 , which would be necessary for convergence. If fact, the limit of the expectation would be π ( k 1 ) 2 x k 2 x \pi\frac{(k-1)^{\frac{2}{x}}}{k^{\frac{2}{x}}} , which tends to π \pi as k k \to \infty .

At least, if we are including only the smallest k k th of the shots, then the proportion that is included decreases as k k increases, and hence it is not unreasonable that the enclosing area should tend to 0 0 .

As to Geometry, there is not really a good category for questions about continuous random variables. The question would probably best sit in Calculus.

Mark Hennings - 4 years, 6 months ago
Kartik Sharma
Dec 11, 2016

Let r r be the minimum radius containing his shots including t = n k t = \left \lfloor\frac{n}{k}\right \rfloor best shots.

Our aim will be to find the probability that the worst among the best t t shots lie within r + d r r+dr and r r .

We select which t t shots are best and then which among those best shot is the one lying between r r and r + d r r + dr .

P(radius containing best t shots is r) = ( n t ) ( t 1 ) ( ( r + d r ) x r x ) r x ( t 1 ) ( 1 r x ) n t \displaystyle \text{P(radius containing best t shots is r)} = \binom{n}{t} \binom{t}{1} ({(r+dr)}^x - r^x) r^{x(t -1)} {(1 - r^x)}^{n-t}

P(radius containing best t shots is r) = ( n t ) t x r x t 1 ( 1 r x ) n t \displaystyle \text{P(radius containing best t shots is r)} = \binom{n}{t} t x r^{xt -1} {(1 - r^x)}^{n-t}

Expected Area = π 0 1 r t x + 1 ( 1 r x ) n t d t 0 1 r t x 1 ( 1 r x ) n t d t \displaystyle \text{Expected Area} = \frac{\pi \int_0^1 {r^{tx +1} {(1 - r^x)}^{n-t} \ dt}}{ \int_0^1 {r^{tx -1} {(1 - r^x)}^{n-t} \ dt}}

= π 0 1 u t 1 + 2 / x ( 1 u ) n t d t 0 1 u t 1 ( 1 u ) n t d t \displaystyle = \frac{\pi \int_0^1 {u^{t-1 +2/x} {(1 - u)}^{n-t} \ dt}}{\int_0^1 {u^{t -1} {(1 - u)}^{n-t} \ dt}}

= π Γ ( t + 2 x ) Γ ( n t + 1 ) Γ ( n + 2 x + 1 ) Γ ( n + 1 ) Γ ( t ) Γ ( n t + 1 ) \displaystyle = \frac{\pi \Gamma\left(t + \frac{2}{x}\right)\Gamma(n-t+1)}{\Gamma\left(n +\frac{2}{x}+1\right)} \frac{\Gamma(n+1)}{\Gamma(t) \Gamma(n-t+1)}

= π Γ ( n k + 2 x ) Γ ( n + 1 ) Γ ( n + 2 x + 1 ) Γ ( n k ) \displaystyle = \frac{\pi \Gamma\left(\left \lfloor\frac{n}{k}\right \rfloor + \frac{2}{x}\right) \Gamma(n+1)}{\Gamma\left(n +\frac{2}{x}+1\right)\Gamma(\left \lfloor\frac{n}{k}\right \rfloor)}

We have to find this expected area as n n \to \infty .

Now we invoke this (something I recently discovered)-

lim n Γ ( p m + n + 1 ) Γ ( q m + n t + 1 ) Γ ( q m + n + 1 ) Γ ( p m + n t + 1 ) = 1 t p q / m \displaystyle \lim_{n \to \infty}{\dfrac{\Gamma\left(\frac{p}{m}+n+1\right) \Gamma\left(\frac{q}{m} + \frac{n}{t} + 1\right)}{\Gamma\left(\frac{q}{m}+n+1\right) \Gamma\left(\frac{p}{m} + \frac{n}{t} + 1\right)}} = \dfrac{1}{t^{|p-q|/m}}

Therefore,

lim n ( Expected Area ) = f ( x , k ) = π k 2 / x \displaystyle \lim_{n \to \infty}{\left(\text{Expected Area}\right)} = f(x,k) = \frac{\pi}{k^{2/x}}

f ( 2 m , x ) = π k m \displaystyle f\left(\frac{2}{m}, x\right) = \frac{\pi}{k^m}

k 2 m = 1 π k 2 m = k 2 π k 2 1 \displaystyle \sum_{k\geq 2}{ \sum_{m=1}^{\infty}{\frac{\pi}{k^{2m}}}} = \sum_{k \geq 2}{\frac{\pi}{k^2 -1}}

which telescopes to

3 π 4 \frac{3\pi}{4}

See my comments above.

Mark Hennings - 4 years, 6 months ago

This needs either to be corrected or deleted, don't you agree?

Mark Hennings - 4 years, 5 months ago

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I have corrected it now. Thanks, I finally understand it completely. Pls check if anything is wrong.

Kartik Sharma - 4 years, 5 months ago

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The first sentence neglects the worst n / k n/k , instead of only including the best n / k n/k . Otherwise, fine.

Mark Hennings - 4 years, 5 months ago

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