Expected time to roll all 1 through 6 on a die
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What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.
I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$
Can someone please explain this to me, possibly with a link to a general topic?
probability dice coupon-collector
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add a comment |
$begingroup$
What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.
I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$
Can someone please explain this to me, possibly with a link to a general topic?
probability dice coupon-collector
$endgroup$
21
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
13
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
1
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If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36
add a comment |
$begingroup$
What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.
I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$
Can someone please explain this to me, possibly with a link to a general topic?
probability dice coupon-collector
$endgroup$
What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.
I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$
Can someone please explain this to me, possibly with a link to a general topic?
probability dice coupon-collector
probability dice coupon-collector
edited Apr 30 '17 at 4:30
Community♦
1
1
asked Mar 24 '11 at 23:42
eternalmatteternalmatt
525158
525158
21
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
13
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
1
$begingroup$
If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36
add a comment |
21
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
13
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
1
$begingroup$
If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36
21
21
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
13
13
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
1
1
$begingroup$
If anyone's curious, simulating reveals $E[$
time until all values rolled$]$ for two dice is roughly $61.2$.$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
If anyone's curious, simulating reveals $E[$
time until all values rolled$]$ for two dice is roughly $61.2$.$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$
Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.
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$begingroup$
Follow-up question, Didier: what's the distribution? :)
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– Jérémie
May 8 '12 at 23:20
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@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
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@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
add a comment |
$begingroup$
Here's the logic:
The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.
You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.
This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$
$endgroup$
add a comment |
$begingroup$
Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.
Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.
Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.
To calculate $E[X_i]$, consider the following,
after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.
Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.
It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.
Hence $E[X]=14.7$.
$endgroup$
add a comment |
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3 Answers
3
active
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3 Answers
3
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$
Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.
$endgroup$
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
add a comment |
$begingroup$
The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$
Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.
$endgroup$
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
add a comment |
$begingroup$
The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$
Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.
$endgroup$
The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$
Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.
edited Jun 11 '16 at 16:11
answered Mar 24 '11 at 23:47
DidDid
248k23225463
248k23225463
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
add a comment |
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
Follow-up question, Didier: what's the distribution? :)
$endgroup$
– Jérémie
May 8 '12 at 23:20
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie Somehow my post gives the answer but if need be, just ask a new question.
$endgroup$
– Did
May 8 '12 at 23:42
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
$begingroup$
@Jérémie: See math.stackexchange.com/questions/379525.
$endgroup$
– joriki
May 14 '16 at 18:01
add a comment |
$begingroup$
Here's the logic:
The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.
You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.
This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$
$endgroup$
add a comment |
$begingroup$
Here's the logic:
The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.
You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.
This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$
$endgroup$
add a comment |
$begingroup$
Here's the logic:
The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.
You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.
This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$
$endgroup$
Here's the logic:
The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.
You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.
This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$
answered Mar 24 '11 at 23:52
Alex BeckerAlex Becker
49.1k698161
49.1k698161
add a comment |
add a comment |
$begingroup$
Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.
Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.
Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.
To calculate $E[X_i]$, consider the following,
after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.
Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.
It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.
Hence $E[X]=14.7$.
$endgroup$
add a comment |
$begingroup$
Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.
Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.
Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.
To calculate $E[X_i]$, consider the following,
after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.
Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.
It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.
Hence $E[X]=14.7$.
$endgroup$
add a comment |
$begingroup$
Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.
Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.
Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.
To calculate $E[X_i]$, consider the following,
after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.
Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.
It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.
Hence $E[X]=14.7$.
$endgroup$
Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.
Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.
Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.
To calculate $E[X_i]$, consider the following,
after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.
Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.
It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.
Hence $E[X]=14.7$.
edited Dec 24 '17 at 13:07
answered Dec 24 '17 at 7:10
souravsourav
284
284
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21
$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49
13
$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34
1
$begingroup$
If anyone's curious, simulating reveals $E[$
time until all values rolled$]$ for two dice is roughly $61.2$.$endgroup$
– ninjagecko
Jun 8 '11 at 20:35
$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36