how to generate random numbers that follow skew normal distribution in matlab
By : Steven
Date : March 29 2020, 07:55 AM
I wish this help you Can't vouch for their performance/adequacy, but http://azzalini.stat.unipd.it/SN/ says the following, and has a link to a .zip file of MATLAB functions: code :
Function RandSkew(fAlpha As Single, _
Optional fLocation As Single = 0, _
Optional fScale As Single = 1, _
Optional bVolatile As Boolean = False) As Single
' shg 20080919
' http://azzalini.stat.unipd.it/SN/faq.html
' Returns a random variable with skewed distribution
' fAlpha = skew
' fLocation = location
' fScale > 0 = scale
Dim sigma As Single
Dim afRN() As Single
Dim u0 As Single
Dim v As Single
Dim u1 As Single
If bVolatile Then Application.Volatile
Randomize (Timer)
sigma = fAlpha / Sqr(1 + fAlpha ^ 2)
afRN = RandNorm()
u0 = afRN(1)
v = afRN(2)
u1 = sigma * u0 + Sqr(1  sigma ^ 2) * v
RandSkew = IIf(u0 >= 0, u1, u1) * fScale + fLocation
End Function

R Creating Normal Distribution Plot using dataset
By : mniedero
Date : March 29 2020, 07:55 AM
may help you . Im new to R. code :
require(ggplot2)
qplot(meanOfSampleMeansVector,propDensity,geom="line")+
xlab("x value")+ylab("Density")+
ggtitle("Sample Means of Exponential Distribution")

Normal distribution function:determine probability of a given point in Java
By : user2987955
Date : March 29 2020, 07:55 AM
like below fixes the issue First of all, the question cannot be answered as is because in a continous distribution like the normal distribution, the probability of an specific point is always zero. You need to ask yourself what it is exactly you want to know in terms of an interval. For example, cern.jet.stat.Probability.normal(double) will answer the question "What is the probability of the value being less than my value?" (Less than or equal is equivalent in this context.) code :
xn = (x  mean) / standard deviation
2 * CPD( abs(xn) )

Does a dataset need to be a normal distribution for every parameter?
By : Bima
Date : March 29 2020, 07:55 AM
I wish this help you As cel said, every model has its own assumptions and limitations. While there might be a model that can only learn on completely normally distributed data  there are plenty of models which don't, such as SVMs or Random Forests. In practice if you know that your data does not conform to the assumptions of your model you could consider using a different model or to manipulate your data to fit your assumption. The latter option is something that you should consider carefully to make sure your manipulation does not render your model useless when used in reallife scenarios.

example algorithm for generating random value in dataset with normal distribution?
By : user3933725
Date : March 29 2020, 07:55 AM
I wish this helpful for you BoxMuller transform in a nutshell: First, get two independent, uniform random numbers from the interval (0, 1], call them U and V.

