normal-distribution

I'm trying to make a histogram in [R], and the normal curve that describes the histogram as follows: w<-rnorm(1000) hist(w,col="red",freq=F,xlim=c(-5,5)) curve(dnorm(w),-5,5,add=T,col="blue") But when I try to plot the normal curve by curve function shows me the following error: Error en curve(dnorm(w), -5, 5, add = T, col = "blue") : 'expr' must be a function, or a call or an expression containing 'x' What am I doing wrong? You just need to drop the "w" argument to dnorm in curve : w<-rnorm(1000) hist(w,col="red",freq=F,xlim=c(-5,5)) curve(dnorm,-5,5,add=T,col="blue") To use something other

Matlab has the function randn to draw from a normal distribution e.g. x = 0.5 + 0.1*randn() draws a pseudorandom number from a normal distribution of mean 0.5 and standard deviation 0.1. Given this, is the following Matlab code equivalent to sampling from a normal distribution truncated at 0 at 1? while x <=0 || x > 1 x = 0.5 + 0.1*randn(); end Using MATLAB's Probability Distribution Objects makes sampling from truncated distributions very easy. You can use the makedist and truncate functions to define the object and then modify (truncate it) to prepare the object for the random function which

I want generate a number in Gaussian and Uniform distributions in matlab. I know this function randi and rand() but all of them are in normal (Gaussian) distribution. How can a generate a random number in uniform distribution? Use rand(dimensions) for a Uniform Distribution between 0 and 1. Use randn(dimensions) * sqrt(sigma) + mu for a Gaussian Distribution with a mean of mu and variance of sigma . randn is the function to generate Gaussian distributed variables ( randi and rand produce uniformly distributed ones). raj You can generate any distribution from rand(). For example , lets say you

I'm trying to simulate the arrival of fans to a stadium. The system itself, I believe it won't be a problem, but, the arrival of the fans follows a normal distribution. My problem is: I have a certain time for the arrival like 100 minutes and 1000 fans, and I need to generate arrivals of Fans at a time following that distribution like -> fan x arrived at 25 minutes, fan y arrived at 54 minutes, and so on. How can I generate these random numbers following a normal distribution? I'm doing this in Java and found the nextGaussian() method in the Random class , but I'm not sure how to use this in

I'm trying to use boost::normal_distribution in order to generate a normal distribution with mean 0 and sigma 1. The following code doesn't work as some values are over or beyond -1 and 1 (and shouldn't be). Could someont point out what I am doing wrong? #include <boost/random.hpp> #include <boost/random/normal_distribution.hpp> int main() { boost::mt19937 rng; // I don't seed it on purpouse (it's not relevant) boost::normal_distribution<> nd(0.0, 1.0); boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > var_nor(rng, nd); int i = 0; for (; i < 10; ++i) { double d = var_nor

I'm trying to use the C++ STD TechnicalReport1 extensions to generate numbers following a normal distribution, but this code (adapted from this article ): mt19937 eng; eng.seed(SEED); normal_distribution<double> dist; // XXX if I use the one below it exits the for loop // uniform_int<int> dist(1, 52); for (unsigned int i = 0; i < 1000; ++i) { cout << "Generating " << i << "-th value" << endl; cout << dist(eng) << endl; } only prints 1 "Generating..." log message, then never exits the for loop ! If I use the distribution I commented out instead, it terminates, so I'm wondering what I'm doing