Variation of Polya’s urn problem?
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I was asked this question at an interview and was stumped by it. The problem is as follows:
Alice is playing an online game. She wins the first round and loses the second. The probability she then wins subsequent rounds is proportional to the number of wins. Calculate the probability that after 100 games, the number of wins and losses are equal.
I am unsure of how to proceed. At the interview, I was originally thinking of using a simple sum of
$$sum_i alpha w_i delta_i,$$
where $alpha$ is normalisation constant, $w_i$ is the number of wins at the $i$th game and $delta_i$ is either 0 or 1. However, I doubt this is a correct approach, and $w_i$ is dependent on the $delta$’s for $j<i$.
I was discussing with a mathematician friend who said that this is a possible variation of Polya’s urn, but I can’t see the connection. Could anyone help me with this visualisation and provide hints on how to move forward? Thank you!
probability probability-theory statistics game-theory polya-urn-model
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up vote
2
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I was asked this question at an interview and was stumped by it. The problem is as follows:
Alice is playing an online game. She wins the first round and loses the second. The probability she then wins subsequent rounds is proportional to the number of wins. Calculate the probability that after 100 games, the number of wins and losses are equal.
I am unsure of how to proceed. At the interview, I was originally thinking of using a simple sum of
$$sum_i alpha w_i delta_i,$$
where $alpha$ is normalisation constant, $w_i$ is the number of wins at the $i$th game and $delta_i$ is either 0 or 1. However, I doubt this is a correct approach, and $w_i$ is dependent on the $delta$’s for $j<i$.
I was discussing with a mathematician friend who said that this is a possible variation of Polya’s urn, but I can’t see the connection. Could anyone help me with this visualisation and provide hints on how to move forward? Thank you!
probability probability-theory statistics game-theory polya-urn-model
1
Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I was asked this question at an interview and was stumped by it. The problem is as follows:
Alice is playing an online game. She wins the first round and loses the second. The probability she then wins subsequent rounds is proportional to the number of wins. Calculate the probability that after 100 games, the number of wins and losses are equal.
I am unsure of how to proceed. At the interview, I was originally thinking of using a simple sum of
$$sum_i alpha w_i delta_i,$$
where $alpha$ is normalisation constant, $w_i$ is the number of wins at the $i$th game and $delta_i$ is either 0 or 1. However, I doubt this is a correct approach, and $w_i$ is dependent on the $delta$’s for $j<i$.
I was discussing with a mathematician friend who said that this is a possible variation of Polya’s urn, but I can’t see the connection. Could anyone help me with this visualisation and provide hints on how to move forward? Thank you!
probability probability-theory statistics game-theory polya-urn-model
I was asked this question at an interview and was stumped by it. The problem is as follows:
Alice is playing an online game. She wins the first round and loses the second. The probability she then wins subsequent rounds is proportional to the number of wins. Calculate the probability that after 100 games, the number of wins and losses are equal.
I am unsure of how to proceed. At the interview, I was originally thinking of using a simple sum of
$$sum_i alpha w_i delta_i,$$
where $alpha$ is normalisation constant, $w_i$ is the number of wins at the $i$th game and $delta_i$ is either 0 or 1. However, I doubt this is a correct approach, and $w_i$ is dependent on the $delta$’s for $j<i$.
I was discussing with a mathematician friend who said that this is a possible variation of Polya’s urn, but I can’t see the connection. Could anyone help me with this visualisation and provide hints on how to move forward? Thank you!
probability probability-theory statistics game-theory polya-urn-model
probability probability-theory statistics game-theory polya-urn-model
edited Nov 18 at 19:34
asked Nov 18 at 10:22
user107224
378214
378214
1
Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14
add a comment |
1
Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14
1
1
Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14
add a comment |
1 Answer
1
active
oldest
votes
up vote
4
down vote
accepted
Let $W_i$ and $L_i$ denote the number of wins and losses after the $i$-th rounds. Then, assuming that the probability of winning the $(i+1)$-th round is $W_i/(W_i+L_i)$,
begin{align}
&mathsf{P}(W_{100}=L_{100}mid W_2=L_2) \
&qquad=binom{98}{49}frac{W_2(W_2+1)cdots (W_2+48)times L_2(L_2+1)cdots (L_2+48)}{(W_2+L_2)(W_2+L_2+1)cdots(W_2+L_2+97)} \
&qquad=binom{98}{49}frac{(1cdot 2cdots 48cdot 49)^2}{2cdot 3cdots 98cdot 99}=frac{1}{99}.
end{align}
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
4
down vote
accepted
Let $W_i$ and $L_i$ denote the number of wins and losses after the $i$-th rounds. Then, assuming that the probability of winning the $(i+1)$-th round is $W_i/(W_i+L_i)$,
begin{align}
&mathsf{P}(W_{100}=L_{100}mid W_2=L_2) \
&qquad=binom{98}{49}frac{W_2(W_2+1)cdots (W_2+48)times L_2(L_2+1)cdots (L_2+48)}{(W_2+L_2)(W_2+L_2+1)cdots(W_2+L_2+97)} \
&qquad=binom{98}{49}frac{(1cdot 2cdots 48cdot 49)^2}{2cdot 3cdots 98cdot 99}=frac{1}{99}.
end{align}
add a comment |
up vote
4
down vote
accepted
Let $W_i$ and $L_i$ denote the number of wins and losses after the $i$-th rounds. Then, assuming that the probability of winning the $(i+1)$-th round is $W_i/(W_i+L_i)$,
begin{align}
&mathsf{P}(W_{100}=L_{100}mid W_2=L_2) \
&qquad=binom{98}{49}frac{W_2(W_2+1)cdots (W_2+48)times L_2(L_2+1)cdots (L_2+48)}{(W_2+L_2)(W_2+L_2+1)cdots(W_2+L_2+97)} \
&qquad=binom{98}{49}frac{(1cdot 2cdots 48cdot 49)^2}{2cdot 3cdots 98cdot 99}=frac{1}{99}.
end{align}
add a comment |
up vote
4
down vote
accepted
up vote
4
down vote
accepted
Let $W_i$ and $L_i$ denote the number of wins and losses after the $i$-th rounds. Then, assuming that the probability of winning the $(i+1)$-th round is $W_i/(W_i+L_i)$,
begin{align}
&mathsf{P}(W_{100}=L_{100}mid W_2=L_2) \
&qquad=binom{98}{49}frac{W_2(W_2+1)cdots (W_2+48)times L_2(L_2+1)cdots (L_2+48)}{(W_2+L_2)(W_2+L_2+1)cdots(W_2+L_2+97)} \
&qquad=binom{98}{49}frac{(1cdot 2cdots 48cdot 49)^2}{2cdot 3cdots 98cdot 99}=frac{1}{99}.
end{align}
Let $W_i$ and $L_i$ denote the number of wins and losses after the $i$-th rounds. Then, assuming that the probability of winning the $(i+1)$-th round is $W_i/(W_i+L_i)$,
begin{align}
&mathsf{P}(W_{100}=L_{100}mid W_2=L_2) \
&qquad=binom{98}{49}frac{W_2(W_2+1)cdots (W_2+48)times L_2(L_2+1)cdots (L_2+48)}{(W_2+L_2)(W_2+L_2+1)cdots(W_2+L_2+97)} \
&qquad=binom{98}{49}frac{(1cdot 2cdots 48cdot 49)^2}{2cdot 3cdots 98cdot 99}=frac{1}{99}.
end{align}
edited Nov 18 at 22:59
answered Nov 18 at 20:17
d.k.o.
8,189527
8,189527
add a comment |
add a comment |
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Saying the probability in subsequent rounds is proportional to the number of wins is not sufficient. It might be fixed at $0$. I think this is usually asked with the probability in each round being the fraction of wins up to that point, so for round $3$ she has $frac 12$ chance to win and if she wins in round $3$ she has $frac 23$ chance to win round $4$.
– Ross Millikan
Nov 18 at 19:55
@RossMillikan I see – could you talk a bit more about that? I’m curious!
– user107224
Nov 18 at 19:57
This is exactly the Polya urn model. en.wikipedia.org/wiki/P%C3%B3lya_urn_model See the 2nd paragraph. Then interpret a black ball as WIN and a white ball as LOSE. $Prob(WIN) = $ exactly the fraction of black balls = fraction of previous wins. Having said this, I don't know how to solve this model but I suspect some googling would reveal quite a lot of resources.
– antkam
Nov 18 at 20:14