normal-distribution

Is there a way to generate a data set with normally distributed random values in R without using a loop? Each entry would represent an independent random variable with a normal distribution. To create an N by M matrix of iid normal random variables type this: matrix( rnorm(N*M,mean=0,sd=1), N, M) tweak the mean and standard deviation as desired. let mu be a vector of means and sigma a vector of standard devs mu<-1:10 sigma<-10:1 sample.size<-100 norm.mat<-mapply(function(x,y){rnorm(x,y,n=sample.size)},x=mu,y=sigma) would produce a matrix with columns holding the relevant samples You can use:

I am using scipy.optimize.fmin_l_bfgs_b to solve a gaussian mixture problem. The means of mixture distributions are modeled by regressions whose weights have to be optimized using EM algorithm. sigma_sp_new, func_val, info_dict = fmin_l_bfgs_b(func_to_minimize, self.sigma_vector[si][pj], args=(self.w_vectors[si][pj], Y, X, E_step_results[si][pj]), approx_grad=True, bounds=[(1e-8, 0.5)], factr=1e02, pgtol=1e-05, epsilon=1e-08) But sometimes I got a warning 'ABNORMAL_TERMINATION_IN_LNSRCH' in the information dictionary: func_to_minimize value = 1.14462324063e-07 information dictionary: {'task':

Given the start and the end of an integer range, how do I calculate a normally distributed random integer between this range? I realize that the normal distribution goes into -+ infinity. I guess the tails can be cutoff, so when a random gets computed outside the range, recompute. This elevates the probability of integers in the range, but as long as the this effect is tolerable (<5%), it's fine. public class Gaussian { private static bool uselast = true; private static double next_gaussian = 0.0; private static Random random = new Random(); public static double BoxMuller() { if (uselast) {

I want to produce 100 random numbers with normal distribution (with µ=10, σ=7) and then draw a quantity diagram for these numbers. How can I produce random numbers with a specific distribution in Excel 2010? One more question: When I produce, for example, 20 random numbers with RANDBETWEEN(Bottom,Top) , the numbers change every time the sheet recalculates. How can I keep this from happening? Excellll 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

I've been hunting for a convenient way to sample from a multivariate normal distribution. Does anyone know of a readily available code snippet to do that? For matrices/vectors, I'd prefer to use Boost or Eigen or another phenomenal library I'm not familiar with, but I could use GSL in a pinch. I'd also like it if the method accepted nonnegative -definite covariance matrices rather than requiring positive-definite (e.g., as with the Cholesky decomposition). This exists in MATLAB, NumPy, and others, but I've had a hard time finding a ready-made C/C++ solution. If I have to implement it myself, I

I want some data to fit the corresponding Gaussian distribution. The data is meant to be Gaussian already, but for some filtering reasons, they will not perfectly match the prescribed and expected Gaussian distribution. I therefore aim to reduce the existing scatter between data and desired distribution. For example, my data fit the Gaussian distribution as follows (the expected mean value is 0 and the standard deviation 0.8): The approximation is already decent, but I really want to crunch the still tangible scatter between simulated data and expected distribution. How can I achieve this?