This is a combined Java and basic math question. The documentation from Random.nextGaussian() states that it samples from a normal distribution with mean 0 and standard deviation 1. What if I wanted to sample from a normal distribution with a different mean and variance?
This form allows you to generate random numbers from a Gaussian distribution (also known as a normal distribution). The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs.
X = randn returns a random scalar drawn from the standard normal distribution. X = randn( n ) returns an n -by- n matrix of normally distributed random numbers.
. A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.
pnorm() This function gives the probability of a normally distributed random number to be less that the value of a given number. It is also called "Cumulative Distribution Function".
The short answer is
Random r = new Random(); double mySample = r.nextGaussian()*desiredStandardDeviation+desiredMean;
For example this answer is given here: http://www.javamex.com/tutorials/random_numbers/gaussian_distribution_2.shtml
I didn't really understand why this worked, but after looking into it a bit I think I figured it out. The mean of the sample point is 0, and the standard deviation is 1; that means that the original sample is also its own z-score ( https://en.wikipedia.org/wiki/Standard_score ). To quote from wikipedia "The absolute value of z represents the distance between the raw score and the population mean in units of the standard deviation". The formula is z=(x-mean)/stdev, so with the default values z=x. If we wanted to retain the z score for the sample but change the mean and stdev what would we do?
z*stdev + mean = x' where z=x, and x' represents the sample from the distribution with the desired mean and standard deviation.
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