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I want to randomly generate 1000 normal variates (using rnorm, e.g.) that have mean 100. 25% of the 1000 numbers should be over 110. How can I do this in R? …

I want to generate r x c contingency tables using Bi-variate normal distribution. Most of the works deals with generating tables from multinomial distribution where the total frequency is fixed. … Suppose if i want to generate r x c tables from Bi-variate normal, Is it necessary that r and c should be equal? If so, How to generate as contingency tables using R? …

Each matrix ($\rho_{\frac{1}{2}}$ in the notation of the first ref.) requires 64 random unit normal variates for its generation. … Should I be able to speed convergence by using low-discrepancy series of normal variates, and if so how might that be effectively accomplished? …

If I simulate the generation of measurements from a number of batches each sampled from log normal distribution but with varying parameters the complete ensemble again comes out log normal. … But can I know just from finding that the total of measurements is log normal that the individual batches also are this way? …

In order to do that, I would like to generate a synthetic log-normal 2D random field. The idea is to extract from it some points with their two dimensional coordinates and (of course) their values. … I do not think that is enough to compute a bi-variate log-normal distribution. Am I right? …

This question is really about R syntax and not about the normal distribution or sampling. So I guess it's 'off topic' here, and may be closed. I'm not quite sure what your difficulty is. … If you set the same seed before each program, then you should fill an $n\times p$ matrix by columns with exactly the same normal variates. n=5; p=2 set.seed(713) # set seed X = matrix(rnorm( …

The matrix-variate normal distribution can be sampled indirectly by utilizing the Cholesky decomposition of two positive definite covariance matrices. … Is it possible to adapt the SVD based sampling technique for the multivariate normal case that overcomes this difficulty to the matrix-variate case? …

FYI, there are much better ways to generate normal variates that do not require inversion of the normal CDF, such as the polar method. …

I want to approximate this distirbution with a multi-variate normal. …

When I plot historic data of productivity it suggest a Normal Distribution, however if I generate a normal random variate I will occasionally obtain negatives or even productivity values over one. … Can I use a Normal distribution -and implement some mechanism for invalid values- or there are more suitable distributions for proportion data? …

In the applications I have in mind (described below), typically the diagonal of $\Sigma$ has a few entries which are $1$ or even as large as $1.5$, so a multivariate Normal will easily generate negative … This R package vignette describes the generation of replicate weights for the "generalized survey bootstrap", and describes how multivariate normal distributions are used to generate replicate weights …

As a rule, I do not use any platform's method to generate lognormal variates, because the conventions about specifying parameters are so varied and confusing. … I always generate normal variates and exponentiate them, because then I know what I'm getting.) import numpy as np from statistics import mean from scipy.stats import norm N = 1000000 m = 1.75917e-7 …

How do I generate random nos based on say k moments? (no other constraints on support) When k = 2, we generate random nos. from a normal distribution defined by the 2 moments. … I have a uni variate sample and no more information about anything. Now, I want to try and simulate nos. from the distribution from which this sample was drawn. …

From https://en.wikipedia.org/wiki/Irwin–Hall_distribution: The generation of pseudo-random numbers having an approximately normal distribution is sometimes accomplished by computing the sum of a number … How would you rescale, and what does “variate” mean here? A normal distribution has infinite support (theoretically if not practically) so it does not seem possible to rescale easily? …

You: Generate an iid uniform time-series Pretend it's Johnson-distributed Apply a transform that converts Johnson variates to standard normal variates, and does who-knows-what to your uniform variates … Fit an arima model to your (IID) data Simulate from an arima with the fitted parameters using normal errors (You didn't change the rand.gen arg in arima.sim; it defaults to standard normal.) …