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Quote: Ace2Avg rolls to get two consecutive 7s is: 6^1 + 6^2 = 42
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Interesting. It leads me to a another puzzle...
What is the expected number of flips of a coin to get two consecutive heads? What if the probability of heads is p? Please don't just reference Ace2's answer as something "previously solved."
Quote: WizardInteresting. It leads me to a another puzzle...
What is the expected number of flips of a coin to get two consecutive heads? What if the probability of heads is p? Please don't just reference Ace2's answer as something "previously solved."
Let E(n) be the expected number of tosses needed to get to 2 consecutive heads from n consecutive heads
E(2) = 0
E(1) = 1 + p E(2) + (1 - p) E(0) = 1 + (1 - p) E(0)
E(0) = 1 + p E(1) + (1 - p) E(0)
p E(0) = 1 + p E(1)
E(0) = 1/p + E(1) = 1/p + 1 + (1 - p) E(0)
p E(0) = (p + 1) / p
E(0) = (p + 1) / p^2 = 1/p + (1/p)^2
For p = 1/2, the expected number is 1 / (1/2) + (1 / (1/2))^2 = 2 + 4 = 6.
Quote: Ace2For an event with probability p, the expected number of trials to get c consecutive occurrences of the event is the sum of (1/p)^n from n= 1 to c.
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I don't dispute this is true. In fact, I can see it is true for n = 1 and n = 2. However, for full credit, you have to prove it.
So, for instance, to roll three consecutive 7s with a pair of dice the avg number rolls is: (6^4 -1) / 5 - 1 = 258
The formula is reduced for a 50/50 coin flip. To get, for example, seven consecutive heads the avg number of flips is simply 2^8 -2 = 254
Quote: Ace2Going along with the current theme of this thread, how many rolls would it take, on average, to roll 18 consecutive yo’s ?
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Good one!
I get 41,660,902,667,961,000,000,000 (please forgive the Excel limit for significant digits)
Assuming one roll per second per person (24/7) and a global population of 8 billion, it would take, on average, 165,019 years to see this happen.
Agreed.Quote: WizardQuote: Ace2Going along with the current theme of this thread, how many rolls would it take, on average, to roll 18 consecutive yo’s ?
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Good one!
I get 41,660,902,667,961,000,000,000 (please for the Excel limit for significant digits)
Assuming one roll per second per person (24/7) and a global population of 8 billion, it would take, on average, 165,019 years to see this happen.
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(18^19 -1) / 17 - 1 =- 4.17 x 10^22 rolls
In this case you could just call it 18^19 / 17.
Those two “minus 1s” don’t have much of a relative effect on a 23 digit answer!
Quote: WizardQuote: Ace2For an event with probability p, the expected number of trials to get c consecutive occurrences of the event is the sum of (1/p)^n from n= 1 to c.
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I don't dispute this is true. In fact, I can see it is true for n = 1 and n = 2. However, for full credit, you have to prove it.
link to original post
Let E(k) be the number of trials needed to get c consecutive occurrences given k consecutive occurrences
E(c) = 0
E(c-1) = 1 + (1 - p) E(0)
E(c-2) = 1 + p E(c-1) + (1 - p) E(0)
= 1 + p (1 + (1 - p) E(0)) + (1 - p) E(0)
= 1 + p + p (1 - p) E(0) + (1 - p) E(0)
= (1 + p) + (p + 1)(1 - p) E(0)
= (1 + p) + (1 - p^2) E(0)
E(c-3) = 1 + p E(c-2) + (1 - p) E(0)
= 1 + p ((1 + p) + (1 - p^2) E(0)) + (1 - p) E(0)
= 1 + p + p^2 + (p - p^3) E(0) + (1 - p) E(0)
= 1 + p + p^2 + (1 - p^3) E(0)
...
E(c-k) = 1 + p + p^2 + ... + p^(k-1) + (1 - p^k) E(0)
...
E(1) = (1 + p + p^2 + ... + p^(c-2)) + (1 - p^(c-1)) E(0)
E(0) = 1 + p E(1) + (1 - p) E(0)
= 1 + p (1 + p + p^2 + ... + p^(c-2)) + E(0) (p - p^c + 1 - p)
= 1 + p (1 + p + p^2 + ... + p^(c-2)) + E(0) (1 - p^c)
p^c E(0) = 1 + p (1 + p + p^2 + ... + p^(c-2))
= 1 + p + p^2 + ... + p^(c - 1)
= (1 - p^c) / (1 - p)
E(0) = (1 - p^c) / (p^c (1 - p))
= ((1 - p^c) / (1 - p)) / p^c
= (p^(c-1) + p^(c-2) + ... + 1) / p^c
= 1/p + 1/p^2 + ... + 1/p^c
The expected number of flips to get c consecutive heads is the c+3 number of the Fibonacci series of order c, plus 1
So, for example, the expected number of flips to get six consecutive heads is the 9th “Hexanacci” number (125) plus 1 is 126.
Quote: ThatDonGuyQuote: WizardQuote: Ace2For an event with probability p, the expected number of trials to get c consecutive occurrences of the event is the sum of (1/p)^n from n= 1 to c.
link to original post
I don't dispute this is true. In fact, I can see it is true for n = 1 and n = 2. However, for full credit, you have to prove it.
link to original post
Let E(k) be the number of trials needed to get c consecutive occurrences given k consecutive occurrences
E(c) = 0
E(c-1) = 1 + (1 - p) E(0)
E(c-2) = 1 + p E(c-1) + (1 - p) E(0)
= 1 + p (1 + (1 - p) E(0)) + (1 - p) E(0)
= 1 + p + p (1 - p) E(0) + (1 - p) E(0)
= (1 + p) + (p + 1)(1 - p) E(0)
= (1 + p) + (1 - p^2) E(0)
E(c-3) = 1 + p E(c-2) + (1 - p) E(0)
= 1 + p ((1 + p) + (1 - p^2) E(0)) + (1 - p) E(0)
= 1 + p + p^2 + (p - p^3) E(0) + (1 - p) E(0)
= 1 + p + p^2 + (1 - p^3) E(0)
...
E(c-k) = 1 + p + p^2 + ... + p^(k-1) + (1 - p^k) E(0)
...
E(1) = (1 + p + p^2 + ... + p^(c-2)) + (1 - p^(c-1)) E(0)
E(0) = 1 + p E(1) + (1 - p) E(0)
= 1 + p (1 + p + p^2 + ... + p^(c-2)) + E(0) (p - p^c + 1 - p)
= 1 + p (1 + p + p^2 + ... + p^(c-2)) + E(0) (1 - p^c)
p^c E(0) = 1 + p (1 + p + p^2 + ... + p^(c-2))
= 1 + p + p^2 + ... + p^(c - 1)
= (1 - p^c) / (1 - p)
E(0) = (1 - p^c) / (p^c (1 - p))
= ((1 - p^c) / (1 - p)) / p^c
= (p^(c-1) + p^(c-2) + ... + 1) / p^c
= 1/p + 1/p^2 + ... + 1/p^c
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A couple of months ago, I also derived the streak calculator for blackjack. Firstly, we define p=win probability, q=loss probability, r=tie probability (thus p+q+r=1), and then we have the average number of hands required for an N success in a row on the p-side event of blackjack outcome.
R=P/(1-r-Pxq),
where P={1-[p/(1-r)]^N}/{1-[p/(1-r)]}.
Over 3.5 +120
Under 3.5 -140
Assuming the number of field goals scored per game follows a normal distribution and that +130/-130 is the fair line for this bet, what is the average number of field goals scored?
-140 vs +120 means that 3.5 is at the 7/13 point of the total - i.e. 7/13 of the area under the normal distribution curve is to the left of 3.5.
Of course, 1/2 of the area is to the left of the mean, so the difference between the mean and 3.5 makes up 1/26 of the total.
This is about 0.096 standard deviations from the mean, so the mean = 3.5 - 0.096 = 3.404
I have had to use a different distribution as, say, you used a normal distribution then it would be possible to have a negative number of field goals! Thus...Quote: Ace2...Assuming the number of field goals scored per game follows a normal distribution and that +130/-130 is the fair line for this bet, what is the average number of field goals scored?
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Quote: ThatDonGuy
-140 vs +120 means that 3.5 is at the 7/13 point of the total - i.e. 7/13 of the area under the normal distribution curve is to the left of 3.5.
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Now the probability of being under 3.5 is 13/23, rather than 7/13, since the bookie is gambling 100 and you are gambling 130 (or the other way round for over). The total value is 230 so to be a fair bet you would win 130 times and the bookie 100 times (or vice versa).
It transpires to get this percentage on a normal curve this is 1/6 SD above mean (I've used data at https://www.mathsisfun.com/data/standard-normal-distribution-table.html )
Solving 3.5 = Av + 1/6 SQRT(Average) means Average = 3.202.
As I said in my reply this includes a finite chance of negative field goals! So perhaps one has to factor this out somehow.
Quote: charliepatrick^ Ace2 and ThatDonGuy
I see your logic and, while I disagree with the approach, interestingly if I were using it would get a very similar answer. My thought would be if the average were Np then the SD would be SQRT(Npq). As N tends to infinity, q tends to 1, so SQRT(Npq) = SQRT(Average).
Now the probability of being under 3.5 is 13/23, rather than 7/13, since the bookie is gambling 100 and you are gambling 130 (or the other way round for over). The total value is 230 so to be a fair bet you would win 130 times and the bookie 100 times (or vice versa).
It transpires to get this percentage on a normal curve this is 1/6 SD above mean (I've used data at https://www.mathsisfun.com/data/standard-normal-distribution-table.html )
Solving 3.5 = Av + 1/6 SQRT(Average) means Average = 3.202.
As I said in my reply this includes a finite chance of negative field goals! So perhaps one has to factor this out somehow.
link to original post
And the answer is very close to the actual 2021 average field goals of 3.213, which makes me more confident of this calculation
Quote: Ace2Quote: ThatDonGuy
-140 vs +120 means that 3.5 is at the 7/13 point of the total - i.e. 7/13 of the area under the normal distribution curve is to the left of 3.5.
link to original postWould you elaborate? I don’t see how you got to 7/13, which does not factor into my calculation
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I assumed -140 vs +120 meant that the -140 event would happen 140 / (140 + 120) = 7/13 of the time.
Hmmm...now that I think about it, that doesn't make much sense, does it? That would mean that if the odds were -260/+240, then the -260 event would happen 26/50 = 52% of the time, when of course it is expected to happen far more often than that.
I have a feeling it should be calculated like this: let f be the amount bet on the favorite, and u the amount bet on the underdog, so that both bets return the same.
Favorite wins: 5/7 f - u
Underdog wins: 6/5 u - f
5/7 f - u = 6/5 u - f
12/7 f = 11/5 u
60 f = 77 u
f = 77, u = 60
The favorite should win 77 / 137 of the time - correct?
Oops - just saw the other responses...
I agree with 77/137 based on the +120/-140 actual line, which would make the fair line 77/60 or +128.33/-128.33, but I wanted to remove that part of the calculation.Quote: ThatDonGuyQuote: Ace2Quote: ThatDonGuy
-140 vs +120 means that 3.5 is at the 7/13 point of the total - i.e. 7/13 of the area under the normal distribution curve is to the left of 3.5.
link to original postWould you elaborate? I don’t see how you got to 7/13, which does not factor into my calculation
link to original post
I assumed -140 vs +120 meant that the -140 event would happen 140 / (140 + 120) = 7/13 of the time.
Hmmm...now that I think about it, that doesn't make much sense, does it? That would mean that if the odds were -260/+240, then the -260 event would happen 26/50 = 52% of the time, when of course it is expected to happen far more often than that.
I have a feeling it should be calculated like this: let f be the amount bet on the favorite, and u the amount bet on the underdog, so that both bets return the same.
Favorite wins: 5/7 f - u
Underdog wins: 6/5 u - f
5/7 f - u = 6/5 u - f
12/7 f = 11/5 u
60 f = 77 u
f = 77, u = 60
The favorite should win 77 / 137 of the time - correct?
Oops - just saw the other responses...
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So, as mentioned, you can assume a fair line of +130/-130, which gives the under a 130/230 chance of winning and the over a 100/230 chance
A 4-player game of Rock, Paper, Scissors is played as follows:
In each turn, each player still in the game "throws" Rock, Paper, or Scissors normally.
If all of the players throw the same thing, no one is eliminated.
If at least one throws Rock, at least one throws Paper, and at least one throws Scissors, no one is eliminated.
If two of the three are thrown, the players who threw the one that would normally lose are eliminated.
For example, if there are four players remaining, and they throw Rock, Rock, Scissors, Rock, then, since "Rock breaks Scissors," the player who threw Scissors is eliminated; if they throw Paper, Paper, Scissors, Paper (or Scissors, Scissors, Paper, Paper), then, since "Scissors cut Paper," the players who threw Paper are eliminated.
What is the expected number of turns before all but one player are eliminated?
Where states f, g and h represent 2, 3 and 4 players remaining respectively
h = 1 + 13/27h + 2/9f + 4/27g
g = 1 + 1/3g + 1/3f
f = 1 + 1/3f
Solve for h
Let "Person 1" always pick Rock, then consider the other player(s)
S1 = 0 (i.e. if you're down to one player, no further turns required)
S2
If player 2 picks Rock, play again, otherwise there's a winner chosen (Paper, Scissors).
x = (x+1)/1 + 1/3 + 1/3 (which we'll rewrite from now on as…
3x = (x+1) + 1 + 1.
So 2x=3, x=1.5.
S3
(i) RR, PS, SP each create a replay (y+1),(y+1),(y+1)
(ii) PP SS RP RS PR SR each have 2v1, where half produce one winner and half produce two to go forward.
9y = 3*(y+1)+3*1+3*(1+x) = 3y+3+3+7.5.
So 6y=13.5, y=2.25
S4
(i) RRR(1), RPS(6), PPS(3), SSP(3) each create a replay (z+1)
(ii) PPP(1) SSS(1) each have 3v1, where half produces one winner and half produce three to go forward
(iii) RPP(3), RSS(3) each have 2v2, so always two go forward
(iv) RRS(3), RRP(3) each have 3v1, where half produces one winner and half produce three to go forward
27z = 13(z+1) + 1*1+1*(y+1) + 6*(x+1) + 3*1+3*(y+1) = 13(z+1) + 1+3.25 + 6*2.5 + 3+3*3.25 =13z + 13+4.25+15+12.75 = 13z+45
14z=45, z=45/14 (or 3 3/14)
btw is there an official solution for the Field Goals problem? - many thanks.
Quote: charliepatrickbtw is there an official solution for the Field Goals problem? - many thanks.
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I'm pretty confident my method is mathematically sound.
Please forgive me, but did I miss it as I couldn't find it on the last few pages of this thread? ThanksQuote: Wizard..I'm pretty confident my method is mathematically sound...link to original post
Quote: charliepatrickPlease forgive me, but did I miss it as I couldn't find it on the last few pages of this thread? ThanksQuote: Wizard..I'm pretty confident my method is mathematically sound...link to original post
link to original post
Maybe we're not talking about the same field goal problem. I'm referring to the one on establishing a fair line on the under 3.5 field goals in the Super Bowl.
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
Quote: acesideI used an online calculator to find out
55986
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That doesn’t make sense since the maximum number is 31.
Quote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
Quote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
Sorta close to:
Quote: Ace2Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
33,102,287,213 / 55,057,397,395, or just over 3/5.
Another brute force solution:
It is easier to calculate the probability of not being able to do it, then subtract that result from 1.
There are 29 unordered 6-tuples of {0, 1, 2, 3, 4, 5} that add up to 18; each one represents a distribution of the rolls.
For example, {4, 4, 4, 3, 3, 0} indicates three numbers rolled 4 times and 2 numbers rolled 3 times.
For each one, calculate the number of specific ways the counts can be assigned to numbers, then calculate the number of ways of getting each particular set.
In this case, there are 6 choices for the zero-rolls number, and for each one, C(5,2) = 10 for the pair of 3-rolls numbers.
Each one has C(18,4) x C(14,4) x C(10,4) x C(6,3) x C(3,3) permutations of the 18 dice.
Calculate each of the 29 values, then add them up and divide by 6^18.
Yes there is a very straight-forward Poisson method for expected number of rolls.Quote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
But not for the extra credit problem. To my knowledge, the Poisson method will only work if the formula is integrated over all time, so any problem that specifies a number of trials is not suitable for it.
Disagree. I get a very different answer, which is supported by a simulationQuote: ThatDonGuyQuote: Ace2Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
33,102,287,213 / 55,057,397,395, or just over 3/5.
Another brute force solution:
It is easier to calculate the probability of not being able to do it, then subtract that result from 1.
There are 29 unordered 6-tuples of {0, 1, 2, 3, 4, 5} that add up to 18; each one represents a distribution of the rolls.
For example, {4, 4, 4, 3, 3, 0} indicates three numbers rolled 4 times and 2 numbers rolled 3 times.
For each one, calculate the number of specific ways the counts can be assigned to numbers, then calculate the number of ways of getting each particular set.
In this case, there are 6 choices for the zero-rolls number, and for each one, C(5,2) = 10 for the pair of 3-rolls numbers.
Each one has C(18,4) x C(14,4) x C(10,4) x C(6,3) x C(3,3) permutations of the 18 dice.
Calculate each of the 29 values, then add them up and divide by 6^18.
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I think that's a coincidence. Hats off to you if you can prove otherwise (possibly with the infinite sum for (pi)^2 / 6 ). That 6 is the only potential link I see to this problemQuote: unJonQuote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
Sorta close to:link to original post2pi^2
Quote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
This is an easy math thread. Can you help show a little more detailed process so that a non-math degree student can understand the whole derivation?
Quote: acesideQuote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
This is an easy math thread. Can you help show a little more detailed process so that a non-math degree student can understand the whole derivation?
link to original post
You mean me? Sure.
Let E(p1, p2, p3, p4, p5, p6) be the expected number of rolls needed to get 6 of at least one number, given p1 1s, p2 2s, p3 3s, p4 4s, p5 5s, and p6 6s have already been rolled.
If any of the p# values = 6, E() = 0.
Otherwise, E(p1, p2, p3, p4, p5, p6) = 1 + 1/6 E(p1 + 1, p2, p3, p4, p5, p6) + 1/6 E(p1, p2 + 1, p3, p4, p5, p6) + 1/6 E(p1, p2, p3 + 1, p4, p5, p6) + 1/6 E(p1, p2, p3, p4 + 1, p5, p6) + 1/6 E(p1, p2, p3, p4, p5 + 1, p6) + 1/6 E(p1, p2, p3, p4, p5, p6 + 1)
The 1 at the start is for the next roll.
Start with E(5, 5, 5, 5, 5, 5) and work backward:
E(5, 5, 5, 5, 5, 5) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + 0) = 1
E(5, 5, 5, 5, 5, 4) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + E(5, 5, 5, 5, 5, 5)) = 7/6
E(5, 5, 5, 5, 5, 3) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + E(5, 5, 5, 5, 5, 4)) = 43/36
and so on, down to E(0, 0, 0, 0, 0, 0), which is the solution as this is the starting point.
Quote: Ace2Disagree. I get a very different answer, which is supported by a simulationQuote: ThatDonGuyQuote: Ace2Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
33,102,287,213 / 55,057,397,395, or just over 3/5.
Another brute force solution:
It is easier to calculate the probability of not being able to do it, then subtract that result from 1.
There are 29 unordered 6-tuples of {0, 1, 2, 3, 4, 5} that add up to 18; each one represents a distribution of the rolls.
For example, {4, 4, 4, 3, 3, 0} indicates three numbers rolled 4 times and 2 numbers rolled 3 times.
For each one, calculate the number of specific ways the counts can be assigned to numbers, then calculate the number of ways of getting each particular set.
In this case, there are 6 choices for the zero-rolls number, and for each one, C(5,2) = 10 for the pair of 3-rolls numbers.
Each one has C(18,4) x C(14,4) x C(10,4) x C(6,3) x C(3,3) permutations of the 18 dice.
Calculate each of the 29 values, then add them up and divide by 6^18.
link to original post
link to original post
You're right; my methodology is correct, but my last division was by (6^18 - the sum) instead of by 6^18.
33,102,287,213 / 88,159,684,608, or 0.375481
So there are 6^6 = 46,656 states, correct?Quote: ThatDonGuyQuote: acesideQuote: ThatDonGuyQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
link to original post
I assume there's a Poisson-based way to solve this, but for now, I used a Markov chain to get:
2,597,868,106,693,535,971 / 131,621,703,842,267,136
or about 19.7374
link to original post
This is an easy math thread. Can you help show a little more detailed process so that a non-math degree student can understand the whole derivation?
link to original post
You mean me? Sure.
Let E(p1, p2, p3, p4, p5, p6) be the expected number of rolls needed to get 6 of at least one number, given p1 1s, p2 2s, p3 3s, p4 4s, p5 5s, and p6 6s have already been rolled.
If any of the p# values = 6, E() = 0.
Otherwise, E(p1, p2, p3, p4, p5, p6) = 1 + 1/6 E(p1 + 1, p2, p3, p4, p5, p6) + 1/6 E(p1, p2 + 1, p3, p4, p5, p6) + 1/6 E(p1, p2, p3 + 1, p4, p5, p6) + 1/6 E(p1, p2, p3, p4 + 1, p5, p6) + 1/6 E(p1, p2, p3, p4, p5 + 1, p6) + 1/6 E(p1, p2, p3, p4, p5, p6 + 1)
The 1 at the start is for the next roll.
Start with E(5, 5, 5, 5, 5, 5) and work backward:
E(5, 5, 5, 5, 5, 5) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + 0) = 1
E(5, 5, 5, 5, 5, 4) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + E(5, 5, 5, 5, 5, 5)) = 7/6
E(5, 5, 5, 5, 5, 3) = 1 + 1/6 x (0 + 0 + 0 + 0 + 0 + E(5, 5, 5, 5, 5, 4)) = 43/36
and so on, down to E(0, 0, 0, 0, 0, 0), which is the solution as this is the starting point.
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Quote: ThatDonGuyQuote: Ace2Disagree. I get a very different answer, which is supported by a simulationQuote: ThatDonGuyQuote: Ace2Extra credit: What is the probability you can accomplish this in 18 rolls or less?
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33,102,287,213 / 55,057,397,395, or just over 3/5.
Another brute force solution:
It is easier to calculate the probability of not being able to do it, then subtract that result from 1.
There are 29 unordered 6-tuples of {0, 1, 2, 3, 4, 5} that add up to 18; each one represents a distribution of the rolls.
For example, {4, 4, 4, 3, 3, 0} indicates three numbers rolled 4 times and 2 numbers rolled 3 times.
For each one, calculate the number of specific ways the counts can be assigned to numbers, then calculate the number of ways of getting each particular set.
In this case, there are 6 choices for the zero-rolls number, and for each one, C(5,2) = 10 for the pair of 3-rolls numbers.
Each one has C(18,4) x C(14,4) x C(10,4) x C(6,3) x C(3,3) permutations of the 18 dice.
Calculate each of the 29 values, then add them up and divide by 6^18.
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You're right; my methodology is correct, but my last division was by (6^18 - the sum) instead of by 6^18.
33,102,287,213 / 88,159,684,608, or 0.375481
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My method was:
The probability (a) of any single number hitting six or more times is ~0.0652651. You get this by calculating the chance it hits 0-5 times and taking the complement. There are c(6,1) = 6 ways to choose the number
The probability (b) of any pair of numbers hitting six or more times is ~0.00107418. There are only 16 distinct ways this can happen. There are c(6,2) = 15 ways to choose the pair
The probability (c) of any trio of numbers hitting six times is ~0.000000168897. There are c(6,3) = 20 ways to choose the trio
Using inclusion-exclusion:
6a - 15b + 20c =~ 0.375481
I doubt it, as some of the states are logically the same; for instance you haveQuote: Ace2So there are 6^6 = 46,656 states, correct?
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555555
555554 (as, for the times it takes to finish, it doesn't matter exactly which of the six numbers has 4 gone).
555544
555444
554444
544444
444444
etc.
000011
000001
000000
[{(x/6)^5/120 + (x/6)^4/24 + (x/6)^3/6 + (x/6)^2/2 + (x/6) + 1} * e^(-x/6)]^6
= 2597868106693535971 / 131621703842267136
=~ 19.74 rolls
Using the very useful property that the average time for x to occur equals the sum of the probabilities that x has not occurred over all time. The above formula sets all six numbers to 0-5 occurrences.
I think this is analogous to the geometric series which tells us, for example, that the expected rolls for any single die number to appear is: 1 / (1 - 5/6) = 6 = (5/6)^0 + (5/6)^1 + (5/6)^2 + (5/6)^3...which is the sum of probabilities it has not occurred over all time
Quote: Ace2So there are 6^6 = 46,656 states, correct?
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Actually, there are twice as many, but the other 46,656 states are the ones where one of the numbers has been rolled 6 times, so the expected number of rolls needed for each of those is zero.
For example:
P(5, 5, 5, 5, 5, 5) = 1 + 1/6 P(6, 5, 5, 5, 5, 5) + 1/6 P(5, 6, 5, 5, 5, 5) + 1/6 P(5, 5, 6, 5, 5, 5) + 1/6 P(5, 5, 5, 6, 5, 5) + 1/6 P(5, 5, 5, 5, 6, 5) + + 1/6 P(5, 5, 5, 5, 5, 6) = 1 + 0 + 0 + 0 + 0 + 0 + 0 = 1.
Quote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
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2597868106693535971 / 131621703842267136 = Approximation: 19.73738396371749
The expression is the integral from 0 to infinity of (exp(-x/6)*(1+x/6+x^2/72+x^3/1296+x^4/31104+x^5/933120))^6 dx.
p.s. Damn, I see Ace2 already posted the answer. I know I'm late, but can I at least get half credit?
Actually, you deserve at least three times that! So I'm giving you:Quote: WizardQuote: Ace2If you roll a single die until one of the six numbers has appeared six times, what is the expected number of rolls?
Extra credit: What is the probability you can accomplish this in 18 rolls or less?
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2597868106693535971 / 131621703842267136 = Approximation: 19.73738396371749
The expression is the integral from 0 to infinity of (exp(-x/6)*(1+x/6+x^2/72+x^3/1296+x^4/31104+x^5/933120))^6 dx.
p.s. Damn, I see Ace2 already posted the answer. I know I'm late, but can I at least get half credit?
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(-1/2)! credits
(Updated with the proper link to the solutions that don't use decimals.)
Quote: MichaelBluejay...19...
Nice try but, I think you're only supposed to use Four 4's!Quote: Ace24*4 + 4 - 4/4 = 19
"You only need ten rolls to hit the ALL. Its [sic] been hit in 15-17 rolls typically."
The easy math puzzles are:
1) Assuming you win the ALL bet, what's the probability you win with less than 18 rolls?
2) Considering winning bets only, how many rolls does it take, on average, to win the ALL bet
Since it is limited to winning bets, ignore all rolls of 7.
Also, since it is for 17 rolls or less, assume there are 17 rolls, including any rolls that would come after a winning set.
Find all increasing 7-tuples of numbers in {2, 3, 4, 5, 6, 8, 9, 10, 11, 12}. ("Increasing" means no number is greater than the one to its right.)
Let P(2) be the number of times 2 appears in the 17 rolls, P(3) the number of times 3 appears, and so on.
For example, if the 7-tuple is (2, 3, 4, 5, 6, 8, 9), then P(2) = P(3) = ... = P(9) = 2, and P(10) = P(11) = P(12) = 1; if it is (2, 2, 2, 2, 2, 2, 2),, then P(2) = 8 and P(3) = ... = P(12) = 1.
There are 17! / (P(2)! P(3)! ... P(12)!) permutations of these 17 numbers.
For each permutation, the probability of actually rolling those numbers is (1/30)^P(2) * (2/30)^P(3) * ... * (2/30)^P(11) * (1/30)^P(12).
Calculate this value for each permutation, and add them up to get:
235,436,099,899 / 8,649,755,859,375, or about 1 / 36.7393.
The solution is the integral over x from 0 to positive infinity of:
1-((1 - e^(-x/30))(1 - e^(-x/15))(1 - e^(-x/10))(1 - e^(-2/15*x))(1 - e^(-x/6))(1 - e^(-x/5)))^2 dx
which is 5,401,372,918,634,611 / 105,826,178,618,160, or about 51.04