The code below maps the statistical moments (mean, variance, skewness, excess kurtosis) generated by corresponding parameters (a, b, loc, scale) of the Johnson-SU distribution (johnsonsu). For the range of loop values specified in my code below, no parameter configuration results in positive skewness, only negative skewness, even though it should be possible to parameterize the Johnson-SU distribution to be positively-skewed.
Tag: probability-distribution
Write a random number generator that, based on uniformly distributed numbers between 0 and 1, samples from a Lévy-distribution?
I’m completely new to Python. Could someone show me how can I write a random number generator which samples from the Levy Distribution? I’ve written the function for the distribution, but I’m confused about how to proceed further! The random numbers generated by this distribution I want to use them to simulate a 2D random walk. I’m aware that from
How to generate random numbers with predefined probability distribution?
I would like to implement a function in python (using numpy) that takes a mathematical function (for ex. p(x) = e^(-x) like below) as input and generates random numbers, that are distributed according to that mathematical-function’s probability distribution. And I need to plot them, so we can see the distribution. I need actually exactly a random number generator function for
How to find a best fit distribution function for a list of data?
I am aware of many probabilistic functions builted-in Python, with the random module. I’d like to know if, given a list of floats, it would be possible to find the distribution equation that best fits the list? I don’t know if numpy does it, but this function could be compared (not equal, but similar) with the Excel’s “Trend” function. How