This is an interesting question. In general, the best way to know the difference between two python implementations is to inspect the code yourself: import inspect, random str_gauss = inspect.getsource(random.gauss) str_nv=inspect.getsource(random.normalvariate) and then you print each of the strings to see how the sources differ. A quick look at the codes show that not only … Read more
Since this question has garnered a lot of views, I thought I’d post code for the final answer that I found, in part, by posting to the Eigen forums. The code uses Boost for the univariate normal and Eigen for matrix handling. It feels rather unorthodox, since it involves using the “internal” namespace, but it … Read more
The parametrization of truncnorm is complicated, so here is a function that translates the parametrization to something more intuitive: from scipy.stats import truncnorm def get_truncated_normal(mean=0, sd=1, low=0, upp=10): return truncnorm( (low – mean) / sd, (upp – mean) / sd, loc=mean, scale=sd) How to use it? Instance the generator with the parameters: mean, standard deviation, … Read more
nextGaussian() will draw samples from a normal distribution with mean 0 and std-deviation 1, so if you want mean 1 hour and std-deviation 15 minutes you’ll need to call it as nextGaussian()*15+60. From the docs for Random.nextGaussian(): Returns: the next pseudorandom, Gaussian (“normally”) distributed double value with mean 0.0 and standard deviation 1.0 from this … Read more
Use the NORMINV function together with RAND(): =NORMINV(RAND(),10,7) To keep your set of random values from changing, select all the values, copy them, and then paste (special) the values back into the same range. Sample output (column A), 500 numbers generated with this formula:
NORMSINV (mentioned in a comment) is the inverse of the CDF of the standard normal distribution. Using scipy, you can compute this with the ppf method of the scipy.stats.norm object. The acronym ppf stands for percent point function, which is another name for the quantile function. In [20]: from scipy.stats import norm In [21]: norm.ppf(0.95) … Read more
There are many methods to generate Gaussian-distributed numbers from a regular RNG. The Box-Muller transform is commonly used. It correctly produces values with a normal distribution. The math is easy. You generate two (uniform) random numbers, and by applying an formula to them, you get two normally distributed random numbers. Return one, and save the … Read more