Julia: Generate normally distributed random number with restricted range
Question: How can I generate a random number in the interval [0,1] from a Gaussian distribution in Julia?
I gather randn is the way to generate normally distributed random numbers, but the documentation's description of how to specify a range is quite opaque.
random range julia-lang normal-distribution
asked Nov 21 at 3:04
Yly
414312
add a comment |
Question: How can I generate a random number in the interval [0,1] from a Gaussian distribution in Julia?
I gather randn is the way to generate normally distributed random numbers, but the documentation's description of how to specify a range is quite opaque.
random range julia-lang normal-distribution
asked Nov 21 at 3:04
Yly
414312
add a comment |
Question: How can I generate a random number in the interval [0,1] from a Gaussian distribution in Julia?
I gather randn is the way to generate normally distributed random numbers, but the documentation's description of how to specify a range is quite opaque.
random range julia-lang normal-distribution
asked Nov 21 at 3:04
Yly
414312
Question: How can I generate a random number in the interval [0,1] from a Gaussian distribution in Julia?
I gather randn is the way to generate normally distributed random numbers, but the documentation's description of how to specify a range is quite opaque.
random range julia-lang normal-distribution
random range julia-lang normal-distribution
asked Nov 21 at 3:04
Yly
414312
asked Nov 21 at 3:04
Yly
414312
asked Nov 21 at 3:04
Yly
414312
asked Nov 21 at 3:04
Yly
414312
asked Nov 21 at 3:04
Yly
414312
414312
add a comment |
add a comment |
1 Answer
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Use the Distributions package. If you don't already have it:
using Pkg ; Pkg.add("Distributions")
then:
using Distributions
mu = 0 #The mean of the truncated Normal
sigma = 1 #The standard deviation of the truncated Normal
lb = 0 #The truncation lower bound
ub = 1 #The truncation upper bound
d = Truncated(Normal(mu, sigma), lb, ub) #Construct the distribution type
x = rand(d, 100) #Simulate 100 obs from the truncated Normal
or all in one line:
x = rand(Truncated(Normal(0, 1), 0, 1), 100)
answered Nov 21 at 4:06
Colin T Bowers
10k43762
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use the Distributions package. If you don't already have it:
using Pkg ; Pkg.add("Distributions")
then:
using Distributions
mu = 0 #The mean of the truncated Normal
sigma = 1 #The standard deviation of the truncated Normal
lb = 0 #The truncation lower bound
ub = 1 #The truncation upper bound
d = Truncated(Normal(mu, sigma), lb, ub) #Construct the distribution type
x = rand(d, 100) #Simulate 100 obs from the truncated Normal
or all in one line:
x = rand(Truncated(Normal(0, 1), 0, 1), 100)
answered Nov 21 at 4:06
Colin T Bowers
10k43762
add a comment |
Use the Distributions package. If you don't already have it:
using Pkg ; Pkg.add("Distributions")
then:
using Distributions
mu = 0 #The mean of the truncated Normal
sigma = 1 #The standard deviation of the truncated Normal
lb = 0 #The truncation lower bound
ub = 1 #The truncation upper bound
d = Truncated(Normal(mu, sigma), lb, ub) #Construct the distribution type
x = rand(d, 100) #Simulate 100 obs from the truncated Normal
or all in one line:
x = rand(Truncated(Normal(0, 1), 0, 1), 100)
answered Nov 21 at 4:06
Colin T Bowers
10k43762
add a comment |
Use the Distributions package. If you don't already have it:
using Pkg ; Pkg.add("Distributions")
then:
using Distributions
mu = 0 #The mean of the truncated Normal
sigma = 1 #The standard deviation of the truncated Normal
lb = 0 #The truncation lower bound
ub = 1 #The truncation upper bound
d = Truncated(Normal(mu, sigma), lb, ub) #Construct the distribution type
x = rand(d, 100) #Simulate 100 obs from the truncated Normal
or all in one line:
x = rand(Truncated(Normal(0, 1), 0, 1), 100)
answered Nov 21 at 4:06
Colin T Bowers
10k43762
Use the Distributions package. If you don't already have it:
using Pkg ; Pkg.add("Distributions")
then:
using Distributions
mu = 0 #The mean of the truncated Normal
sigma = 1 #The standard deviation of the truncated Normal
lb = 0 #The truncation lower bound
ub = 1 #The truncation upper bound
d = Truncated(Normal(mu, sigma), lb, ub) #Construct the distribution type
x = rand(d, 100) #Simulate 100 obs from the truncated Normal
or all in one line:
x = rand(Truncated(Normal(0, 1), 0, 1), 100)
answered Nov 21 at 4:06
Colin T Bowers
10k43762
answered Nov 21 at 4:06
Colin T Bowers
10k43762
answered Nov 21 at 4:06
Colin T Bowers
10k43762
answered Nov 21 at 4:06
Colin T Bowers
10k43762
10k43762
add a comment |
add a comment |
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