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How to compute (negative binomial) distribution PDF and CDF in C++?
How do you declare an interface in C++?How can I profile C++ code running on Linux?Random numbers from binomial distributionGenerate random numbers with a given (numerical) distributionC++11 introduced a standardized memory model. What does it mean? And how is it going to affect C++ programming?Getting a random draw from the binomial distribution based on a sample statisticHow to calculate the cumulative density function of a sum of random variables in C++?How to random sample lognormal data in Python using the inverse CDF and specify target percentiles?How to succinctly, portably, and thoroughly seed the mt19937 PRNG?Generate random variable with known PDF expression in MATLAB
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STD has many distributions, that apparently are used to generate pseudo random variables, see e.g. below code that generates and outputs some negative binomial distributed numbers.
Now this might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability? If yes, how? I know I could run my own code for that, but I'd rather not do that if it there is some way to get the probabilities from std.
If possible, same question for other distributions, e.g. CDF of gamma distribution.
int main()
std::negative_binomial_distribution<int> negBin 5,0.5 ;//Negative binomial distribution
std::mt19937 RNG(260783);//Random generator
for (size_t i = 0; i < 4; i++)
std::cout << negBin(RNG) << std::endl;
return 0;
c++ random
asked Mar 25 at 8:23
willemwillem
1,27431736
add a comment |
STD has many distributions, that apparently are used to generate pseudo random variables, see e.g. below code that generates and outputs some negative binomial distributed numbers.
Now this might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability? If yes, how? I know I could run my own code for that, but I'd rather not do that if it there is some way to get the probabilities from std.
If possible, same question for other distributions, e.g. CDF of gamma distribution.
int main()
std::negative_binomial_distribution<int> negBin 5,0.5 ;//Negative binomial distribution
std::mt19937 RNG(260783);//Random generator
for (size_t i = 0; i < 4; i++)
std::cout << negBin(RNG) << std::endl;
return 0;
c++ random
asked Mar 25 at 8:23
willemwillem
1,27431736
You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48
add a comment |
STD has many distributions, that apparently are used to generate pseudo random variables, see e.g. below code that generates and outputs some negative binomial distributed numbers.
Now this might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability? If yes, how? I know I could run my own code for that, but I'd rather not do that if it there is some way to get the probabilities from std.
If possible, same question for other distributions, e.g. CDF of gamma distribution.
int main()
std::negative_binomial_distribution<int> negBin 5,0.5 ;//Negative binomial distribution
std::mt19937 RNG(260783);//Random generator
for (size_t i = 0; i < 4; i++)
std::cout << negBin(RNG) << std::endl;
return 0;
c++ random
asked Mar 25 at 8:23
willemwillem
1,27431736
STD has many distributions, that apparently are used to generate pseudo random variables, see e.g. below code that generates and outputs some negative binomial distributed numbers.
Now this might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability? If yes, how? I know I could run my own code for that, but I'd rather not do that if it there is some way to get the probabilities from std.
If possible, same question for other distributions, e.g. CDF of gamma distribution.
int main()
std::negative_binomial_distribution<int> negBin 5,0.5 ;//Negative binomial distribution
std::mt19937 RNG(260783);//Random generator
for (size_t i = 0; i < 4; i++)
std::cout << negBin(RNG) << std::endl;
return 0;
c++ random
c++ random
asked Mar 25 at 8:23
willemwillem
1,27431736
asked Mar 25 at 8:23
willemwillem
1,27431736
asked Mar 25 at 8:23
willemwillem
1,27431736
asked Mar 25 at 8:23
willemwillem
1,27431736
asked Mar 25 at 8:23
willemwillem
1,27431736
1,27431736
You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48
add a comment |
You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48
You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48
You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48
add a comment |
2 Answers
2
active
oldest
votes
The standard doesn't specify how an implementation should implement the distribution, other than that sampling from it should take an amortised constant number of samples from the Generator.
None of the members provide either the CDF or PDF
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
add a comment |
might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability?
In general, no, sampling does not require to know PDF and/or CDF, see f.e. Marsaglia method(s) to sample normally distributed RV.
I could propose to take a look at GNU Scientific Library, it has sampling methods as well as PDF and CDF for negative binomial:
https://www.gnu.org/software/gsl/manual/html_node/The-Negative-Binomial-Distribution.html
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
add a comment |
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2 Answers
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oldest
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2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
The standard doesn't specify how an implementation should implement the distribution, other than that sampling from it should take an amortised constant number of samples from the Generator.
None of the members provide either the CDF or PDF
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
add a comment |
The standard doesn't specify how an implementation should implement the distribution, other than that sampling from it should take an amortised constant number of samples from the Generator.
None of the members provide either the CDF or PDF
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
add a comment |
The standard doesn't specify how an implementation should implement the distribution, other than that sampling from it should take an amortised constant number of samples from the Generator.
None of the members provide either the CDF or PDF
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
The standard doesn't specify how an implementation should implement the distribution, other than that sampling from it should take an amortised constant number of samples from the Generator.
None of the members provide either the CDF or PDF
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
answered Mar 25 at 9:34
CalethCaleth
20.8k22344
20.8k22344
add a comment |
add a comment |
might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability?
In general, no, sampling does not require to know PDF and/or CDF, see f.e. Marsaglia method(s) to sample normally distributed RV.
I could propose to take a look at GNU Scientific Library, it has sampling methods as well as PDF and CDF for negative binomial:
https://www.gnu.org/software/gsl/manual/html_node/The-Negative-Binomial-Distribution.html
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
add a comment |
might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability?
In general, no, sampling does not require to know PDF and/or CDF, see f.e. Marsaglia method(s) to sample normally distributed RV.
I could propose to take a look at GNU Scientific Library, it has sampling methods as well as PDF and CDF for negative binomial:
https://www.gnu.org/software/gsl/manual/html_node/The-Negative-Binomial-Distribution.html
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
add a comment |
might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability?
In general, no, sampling does not require to know PDF and/or CDF, see f.e. Marsaglia method(s) to sample normally distributed RV.
I could propose to take a look at GNU Scientific Library, it has sampling methods as well as PDF and CDF for negative binomial:
https://www.gnu.org/software/gsl/manual/html_node/The-Negative-Binomial-Distribution.html
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
might mean that internally, there is code that computes the CDF and or PDF of the negative binomial distribution, i.e. the probability that the random variable takes on a certain value, e.g. 6. Is there a way to output that probability?
In general, no, sampling does not require to know PDF and/or CDF, see f.e. Marsaglia method(s) to sample normally distributed RV.
I could propose to take a look at GNU Scientific Library, it has sampling methods as well as PDF and CDF for negative binomial:
https://www.gnu.org/software/gsl/manual/html_node/The-Negative-Binomial-Distribution.html
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
answered Mar 25 at 16:47
Severin PappadeuxSeverin Pappadeux
10.8k21535
10.8k21535
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
add a comment |
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
Thanks. I actually ended up using boost, which also has the desired functionaty. Btw I was well aware that sampling does not necesarily require the probs (hence "might"). But since I found on the internet that the boost library, which has similar syntax, does provide the PDF function, I thought I might just have missed something.
– willem
Mar 26 at 7:25
add a comment |
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You can find the probability for standard distributions using the formula for the pmf. See this wiki page for negative binomial. And, see Inverse transform sampling to learn how one can generate samples following a given distribution. I don't know which method the std random is using.
– dhanushka
Mar 25 at 10:48