Mixing
How to simulate the random variable $Y$ using another random variable $X$?
Let $X$ be the discrete uniform random variable on taking values in the set ${1,2,3,4,5}.$ We want to simulate the random variable $Y$ which is the discrete uniform random variable taking values in the set ${1,2,3,4,5,6,7}.$
Let generate_X() be a method which generates the numbers from $1$ to $5$ with uniform probability. My goal is to use this method to write a method generate_Y() which generates numbers $1-7$ with uniform probability.
I am not sure how I can 'stretch' the domain from $1-5$ to $1-7$. Any ideas will be much appreciated.
simulation
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
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Let $X$ be the discrete uniform random variable on taking values in the set ${1,2,3,4,5}.$ We want to simulate the random variable $Y$ which is the discrete uniform random variable taking values in the set ${1,2,3,4,5,6,7}.$
Let generate_X() be a method which generates the numbers from $1$ to $5$ with uniform probability. My goal is to use this method to write a method generate_Y() which generates numbers $1-7$ with uniform probability.
I am not sure how I can 'stretch' the domain from $1-5$ to $1-7$. Any ideas will be much appreciated.
simulation
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
add a comment |
Let $X$ be the discrete uniform random variable on taking values in the set ${1,2,3,4,5}.$ We want to simulate the random variable $Y$ which is the discrete uniform random variable taking values in the set ${1,2,3,4,5,6,7}.$
Let generate_X() be a method which generates the numbers from $1$ to $5$ with uniform probability. My goal is to use this method to write a method generate_Y() which generates numbers $1-7$ with uniform probability.
I am not sure how I can 'stretch' the domain from $1-5$ to $1-7$. Any ideas will be much appreciated.
simulation
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
Let $X$ be the discrete uniform random variable on taking values in the set ${1,2,3,4,5}.$ We want to simulate the random variable $Y$ which is the discrete uniform random variable taking values in the set ${1,2,3,4,5,6,7}.$
Let generate_X() be a method which generates the numbers from $1$ to $5$ with uniform probability. My goal is to use this method to write a method generate_Y() which generates numbers $1-7$ with uniform probability.
I am not sure how I can 'stretch' the domain from $1-5$ to $1-7$. Any ideas will be much appreciated.
simulation
simulation
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
asked Nov 20 '18 at 16:11
Hello_World
3,89321630
3,89321630
add a comment |
add a comment |
1 Answer
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The standard way to do this is rejection sampling. You simply generate k samples of your "d5" such that you have more than 7 total possible outcomes. Now you pick some multiple of 7 of the total outcomes of your k d5 rolls to assign d7 values to. If you get a different outcome, then you restart.
For example, with k=2, you roll two d5, you assign values to 21 of the 25 possible outcomes, giving each of the 7 possible d7 rolls 3 outcomes. You discard the other four possible outcomes: if you get those then you have to retry.
Generalizing this to other situations (besides mapping a discrete uniform distribution into another discrete uniform distribution) requires a significantly different approach. One general approach is to use generate_X() to effectively generate independent Bernoulli(1/2) variables (using exactly this approach, but with a d7 replaced by a d2). Once you have a way to generate such a sequence, you can in principle define generate_Y() in great generality.
answered Nov 20 '18 at 16:17
Ian
67.3k25387
add a comment |
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1 Answer
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1 Answer
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The standard way to do this is rejection sampling. You simply generate k samples of your "d5" such that you have more than 7 total possible outcomes. Now you pick some multiple of 7 of the total outcomes of your k d5 rolls to assign d7 values to. If you get a different outcome, then you restart.
For example, with k=2, you roll two d5, you assign values to 21 of the 25 possible outcomes, giving each of the 7 possible d7 rolls 3 outcomes. You discard the other four possible outcomes: if you get those then you have to retry.
Generalizing this to other situations (besides mapping a discrete uniform distribution into another discrete uniform distribution) requires a significantly different approach. One general approach is to use generate_X() to effectively generate independent Bernoulli(1/2) variables (using exactly this approach, but with a d7 replaced by a d2). Once you have a way to generate such a sequence, you can in principle define generate_Y() in great generality.
answered Nov 20 '18 at 16:17
Ian
67.3k25387
add a comment |
The standard way to do this is rejection sampling. You simply generate k samples of your "d5" such that you have more than 7 total possible outcomes. Now you pick some multiple of 7 of the total outcomes of your k d5 rolls to assign d7 values to. If you get a different outcome, then you restart.
For example, with k=2, you roll two d5, you assign values to 21 of the 25 possible outcomes, giving each of the 7 possible d7 rolls 3 outcomes. You discard the other four possible outcomes: if you get those then you have to retry.
Generalizing this to other situations (besides mapping a discrete uniform distribution into another discrete uniform distribution) requires a significantly different approach. One general approach is to use generate_X() to effectively generate independent Bernoulli(1/2) variables (using exactly this approach, but with a d7 replaced by a d2). Once you have a way to generate such a sequence, you can in principle define generate_Y() in great generality.
answered Nov 20 '18 at 16:17
Ian
67.3k25387
add a comment |
The standard way to do this is rejection sampling. You simply generate k samples of your "d5" such that you have more than 7 total possible outcomes. Now you pick some multiple of 7 of the total outcomes of your k d5 rolls to assign d7 values to. If you get a different outcome, then you restart.
For example, with k=2, you roll two d5, you assign values to 21 of the 25 possible outcomes, giving each of the 7 possible d7 rolls 3 outcomes. You discard the other four possible outcomes: if you get those then you have to retry.
Generalizing this to other situations (besides mapping a discrete uniform distribution into another discrete uniform distribution) requires a significantly different approach. One general approach is to use generate_X() to effectively generate independent Bernoulli(1/2) variables (using exactly this approach, but with a d7 replaced by a d2). Once you have a way to generate such a sequence, you can in principle define generate_Y() in great generality.
answered Nov 20 '18 at 16:17
Ian
67.3k25387
The standard way to do this is rejection sampling. You simply generate k samples of your "d5" such that you have more than 7 total possible outcomes. Now you pick some multiple of 7 of the total outcomes of your k d5 rolls to assign d7 values to. If you get a different outcome, then you restart.
For example, with k=2, you roll two d5, you assign values to 21 of the 25 possible outcomes, giving each of the 7 possible d7 rolls 3 outcomes. You discard the other four possible outcomes: if you get those then you have to retry.
Generalizing this to other situations (besides mapping a discrete uniform distribution into another discrete uniform distribution) requires a significantly different approach. One general approach is to use generate_X() to effectively generate independent Bernoulli(1/2) variables (using exactly this approach, but with a d7 replaced by a d2). Once you have a way to generate such a sequence, you can in principle define generate_Y() in great generality.
answered Nov 20 '18 at 16:17
Ian
67.3k25387
edited Nov 20 '18 at 16:25
answered Nov 20 '18 at 16:17
Ian
67.3k25387
answered Nov 20 '18 at 16:17
Ian
67.3k25387
answered Nov 20 '18 at 16:17
Ian
67.3k25387
67.3k25387
add a comment |
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