distribution

来源： https://stackoverflow.com/questions/63449211/scipy-von-mises-distribution-on-a-half-circle

来源： https://stackoverflow.com/questions/63449211/scipy-von-mises-distribution-on-a-half-circle

问题 This might have been asked lots of times, but still I couldn't find info on why are they needed. I use DEVELOPER prov profiles to test apps on my device, that makes sense. The Provisioning Portal explains prov profiles like this: A Provisioning Profile is a collection of digital assets that uniquely ties developers and devices to an authorized iOS Development Team and enables a device to be used for testing. By this logic they are only needed for testing, eg not for distribution. Do we need

问题 I am trying to fit my data to a Negative Binomial Distribution with the package scipy in Python . However, my validation seems to fail. These are my steps: I have some demand data which is described by the statistics: mu = 1.4 std = 1.59 print(mu, std) I use the parameterization function below, taken from this post to compute the two NB parameters. def convert_params(mu, theta): """ Convert mean/dispersion parameterization of a negative binomial to the ones scipy supports See https://en

问题 In scipy the negative binomial distribution is defined as: nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k This is the common definition, see also wikipedia: https://en.wikipedia.org/wiki/Negative_binomial_distribution However, there exists a different parametrization where the negative Binomial is defined by the mean mu and the dispersion parameter. In R this is easy, as the negbin can be defined by both parametrizations: dnbinom(x, size, prob, mu, log = FALSE) How can I use the mean

问题 I have 4 parts, every part 10000 times, which should fit into case, and the dimensions of the parts are given by uniform, normal and triangular distribution by randomly generating numbers in added dimensions of each distribution. For each 4 parts there is decision if they fit or not. But that shouldn't be a problem. I've managed somehow to do uniform and normal distribution: public double uniformDistrubution(double min, double max) { Random rand = new Random(); return Math.random() * max +

问题 I have 4 parts, every part 10000 times, which should fit into case, and the dimensions of the parts are given by uniform, normal and triangular distribution by randomly generating numbers in added dimensions of each distribution. For each 4 parts there is decision if they fit or not. But that shouldn't be a problem. I've managed somehow to do uniform and normal distribution: public double uniformDistrubution(double min, double max) { Random rand = new Random(); return Math.random() * max +

问题 I have some count data. I want to plot histogram with the count data and add the negative binomial, normal, and Poisson density function but fit the functions to the count data. I tried following this example but (a) I have trouble fitting the negative binomial and poisson functions (b) No where close to scaling it to the count data level (c) Dont know how to fit all three on same graph with legends for each line (d) Also, how can I get basic stats of each fit? for example, the neg binomial

问题 I looked into the question Best fit Distribution plots and found out that answers used the Kolmogorov-Smirnov Test to find the best fit distribution. I also found out that there is an Anderson-Darling test that is also used to get the best fit distribution based on given data. So, I have a few questions: Question 1: If I want to combine both tests, how can I do that where it searches for the maximum p-value of both tests(find the highest p-value and is common in both tests then I extract the

问题 In scipy there is no support for fitting discrete distributions using data. I know there are a lot of subject about this. For example if i have an array like below: x = [2,3,4,5,6,7,0,1,1,0,1,8,10,9,1,1,1,0,0] I couldn' t apply for this array; from scipy.stats import nbinom param = nbinom.fit(x) But i would like to ask you up to date, is there any way to fit for these three discrete distributions and then choose the best fit for the discrete dataset? 回答1: You can use Method of Moments to fit