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I draw $8$ cards randomly from a shuffled deck without replacement. What is the expected value of the sum of the largest $3$ cards? The ace is given a value of 1 and the jack, queen and king are all given a value of $10$. I can generate a simulation answer but that is all. How can this be done, I am stuck?

lsp
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bobbym
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2 Answers2

1

For each of the possible values for the sum, $S$, of the three highest cards -- integers on $[6,30]$ -- figure out the number of combinations of eight-card hands that would give the three highest cards that sum.

For $S=30$, we can have anywhere from three to eight $10$s, so we consider those separately:

$$P(S=30, 10, 10, 10) = \frac{{16 \choose 3}{36 \choose 5} + {16 \choose 4}{36 \choose 4} + {16 \choose 5}{36 \choose 3} + {16 \choose 6}{36 \choose 2} + {16 \choose 7}{36 \choose 1} + {16 \choose 8}{36 \choose 0}}{{52 \choose 8}}.$$

For something a little more complicated, you'll need to add up more combinations. For $S=20$, for example, there are twelve possible triples for the three highest cards:

$$(7, 7, 6), \\ (8, 8, 4), (8, 7, 5), (8, 6, 6), \\ (9, 9, 2), (9, 8, 3), (9, 7, 4), (9, 6, 5), \\ (10, 8, 2), (10, 7, 3), (10, 6, 4), (10, 5, 5).$$

Let's calculate $P(S=20, 9, 8, 3).$ We draw one $9$: ${4 \choose 1}$. We draw one $8$: also ${4 \choose 1}.$ Then there needs to be at least one $3$ (but due to the constraints on the number of $2$s and $3$s, there can't be less than two!):

$$P(S=20, 9, 8, 3) = \frac{{4 \choose 1}{4 \choose 1}\left[{4 \choose 4}{4 \choose 2} + {4 \choose 3}{4 \choose 3} + {4 \choose 2}{4 \choose 4}\right]}{{52 \choose 8}}.$$

(Note that there are little subtleties like the fact that $(10, 9, 1)$ isn't possible. If you draw a $10$ and a $9$, at least two of the remaining six cards must be something higher than an ace.)

Once you have the probability of getting each sum individually, then getting the expected value is easy. Counting up the number of combinations of cards for each sum is the laborious part.

John
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  • Hi John; That is what I tried but to show how tedious that approach is for instance there are 13 partitions of 20. You left out (10,9,1). I had to write a program to get that one. Is there anything easier? – bobbym Jan 29 '14 at 19:54
  • I didn't leave it out. It's not possible. See the second to last paragraph. – John Jan 29 '14 at 19:55
  • Hi; You are correct, I missed that. Sorry. – bobbym Jan 29 '14 at 19:57
  • I don't really see anything easier, but it's not a ridiculously large number of possibilities. It's a good way to while away an afternoon, though. :) That, and you can probably get some code going to determine the possible triplets as well as the number of combinations of each. – John Jan 29 '14 at 20:05
  • Thank you!! That's an excellent catch. I'll fix my answer. – John Jan 30 '14 at 23:04
  • Hi John; Thanks for fixing it. That answer agrees with simulations. Thanks for your help. – bobbym Jan 31 '14 at 08:17
  • There might be a slight problem with your solution to the (9,8,3) problem. The way you pick the 9 and 8 is fine but choosing from 3,3,3,3,2,2,2,2,1,1,1,1 for the last 6 could get 2,2,1,1,1,1 or something like it. Any one of these would not then sum to 20. Do you agree? – bobbym Jan 31 '14 at 17:04
  • Yeah, shoot, that's correct, too. I'm glad you're calling me out on this. It reminds me that I need to think things through a bit more. I'll edit shortly. – John Jan 31 '14 at 17:08
  • Do not worry about that I appreciate everything you have. You have put new life into an approach I gave up on. But when you say there needs to be at least one 3 (but due to the constraints on the number of 3 s and 2 s, there can't be less than two!) aren't you forgetting about the ones? – bobbym Jan 31 '14 at 20:01
1

A little late, but here's a way to compute the answer:

We can first write the order statistic and then find the sum by linearity of expectation.

After sorting, the probability that the $k^{th}$ position is $a$ is given by (derived by considering it as a case of hypergeometric distribution): \begin{align*} \mathbb{P}\left(X_{(k)}=a\right) &= \left.\left(\sum_{j=n-k-n_a+1}^{n-k} \; \sum_{i=n-k-j+1}^{n_a} \dbinom{n_{a}}{i}\, \dbinom{(a-1)n_{a-1}}{n-i-j}\, \dbinom{52-n_a-(a-1)n_{a-1}}{j}\right) \middle/ \dbinom{52}{n} \right. \end{align*}

where:

$n$ : number of cards drawn

$n_a$ : number of cards having face value of $a$

Then, the expected value of the $k^{th}$ card is:

\begin{align*} \mathbb{E}\left(X_{(k)}\right) &= \sum_{a=1}^{10} a\, \mathbb{P}\left(X_{(k)}=a\right) \end{align*}

Hence, the required answer is: \begin{align*} \mathbb{E}\left(X_{(6)}\right)+\mathbb{E}\left(X_{(7)}\right)+\mathbb{E}\left(X_{(8)}\right) &= \boxed{\dfrac{54800638}{1929585}} \approx 28.400219736368 \end{align*}

Here's a Sage code for computation:

n=[4]*10+[16]
nn=8
sum(sum(a*sum(sum([binomial(n[a],i)*binomial(n[a-1]*(a-1),nn-i-j)*binomial(52-n[a]-(a-1)*n[a-1],j) for i in [nn-k-j+1..n[a]]]) for j in [nn-k-n[a]+1..nn-k]) for a in [1..10]) for k in [6..8])/binomial(52,8)
gar
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