Discrete Random Variables

% [z zC Z ZC] = transRV(jointXY,C,opp,n) Numerically approximate the PDF of% a nonlinear combination of the random variables X and Y given the joint% distribution of X and Y. The joint distribution (jointXY) should be% generated using [jointXY, C] = hist3([x(:) y(:)]).%% z =...

This collection generates random variables and vectors of random variables with various distributions, including Bernoulli, geometric, exponential, Gaussian, Poisson, discrete uniform, and continuous uniform, based on the parameters you input to the function.

This function generates random numbers distributed according to the Laplace distribution using the Probability integral transformation


Platforms: Matlab

Fits the Laird-Ware Linear Random Effects Model. This model assumes that for each subject y=x*b+z*g+e where x and z are known m x p and m x r matricies, b is p x 1 parameter vector and g is a r vector which has a multivariate normal distribution with mean zero, e is a vector of identitcal...


Platforms: Matlab

Using the Cholesky decomposition, it generates n iterations of multivariate Gaussian random variables for a given mean vector (mu) and variance-covariance matrix (sigma).mvgrnd(mu,sigma,n)


Platforms: Matlab

Heteroskedasticity is a sequence of random variables, that could affect statistical data if they have large variances.It will need and use the 'regstats' and 'chi2cdf' functions from the Statistics Toolbox. Requirements: - MATLAB 7.7 or higher - MATLAB's Statistics Toolbox


Platforms: Windows, Mac, *nix, Matlab, BSD Solaris

BPSK SYSTEM SIMULATIONWe simulate the generation of random variables r0 and r1, which constitutes the input to detector. We begin by generating a binary sequence of 0?s and 1?s that occur with equal probability and are mutually (statistically) independent. For this, we use a Random number...


Platforms: Matlab

Functions written in 2007 for Master Thesis: "Simulating dependent random variables using copulas. Applications to Finance and Insurance". Functions include MVCOPRND - multivariate copula generator, CMLSTAT for estimation of copula parameters using Canonical Maximum Likelihood Method. Functions...


Platforms: Matlab

This function generate random variables distributed according to a truncated normal distribution (or, by a translation, to a normal distribution with positive support). This kind of problem is especially interesting for generating variables with MCMC methods.We use a mixed accept-reject...


Platforms: Matlab

PoissRatioCdf computes the cumulative distribution function (cdf) and/or the probability mass function (pmf) of the random variable X which is proportional to the ratio of two independent Poisson random variables, X = constant * (Y_lambdaNum / Y_lambdaDen), where constant is given constant,...


Platforms: Matlab

This function generates Pareto random variables (of type I). See "Statistical Distributions", Evans, Hastings and Peacock, Wiley, 1993or http://www.maths.adelaide.edu.au/matthew.r...trns/node6.htmlor http://en.wikipedia.org/wiki/Pareto_distribution The Pareto distribution is a classic...


Platforms: Matlab

This file obtain a Mann-Whitney's U of two random variables with continuous cumulative distribution associated to a p-value. This procedure is highly recommended for sample sizesnx and ny


Platforms: Matlab

This file obtain a Mann-Whitney's U of two random variables with continuous cumulative distribution associated to a p-value. This procedure is highly recommended for sample sizes 7< nx and ny


Platforms: Matlab

Probability of obtaining a Mann-Whitney's U of two random variables with continuous cumulative distribution. Based on the Fortran77 algorithm AS 62 Appl. Statist. (1973). This procedure is highly recommended for sample sizes 7< nx and ny


Platforms: Matlab

Probability of obtaining a Mann-Whitney's U of two random variables with continuous cumulative distribution. It's based on the Mann-Whitney (1947). This procedure is highly recommended for sample sizes n1 and n2


Platforms: Matlab

Generates complex generalized gaussian random variables with augmentedcovariance matrix Ta = [2*s 0; 0 2*s];and shape parameter c, where c = 1 corresponds to the Gaussian case.x = cggd_rand(c,s,N) generates a vector 1xN of complex samples with a circulargaussian distribution with shape parameter...


Platforms: Matlab

Mutual information I(X,Y) measures the degree of dependence (in terms of probability theory) between two random variables X and Y. Is is non-negative and equal to zero when X and Y are mutually independent. Conditional mutual information I(X,Y|Z) is the expected value of I(X,Y) given the value of...


Platforms: Matlab

Y = BERNPDF(P) where P is an N x 1 vector returns the distribution of the sum of N Bernoulli random variables. Y is of length N+1 and Y(j) is equal to the probability that the sum adds to j+1. The vector P comprises the means of Bernoulli random variables, which is to say that P(k) is the...


Platforms: Matlab

Fligner-Policello is a non-parametric test of two combined random variables with continuous cumulative distribution. It is a robust rank-order test of treatment effects for populations which assumes neither normality nor equal variances (Behrens-Fisher problem). It does one assumption, which is...


Platforms: Matlab

The EJS Radioactive Decay Distribution Model simulates the decay of a radioactive sample using discrete random events. It displays the distribution of the number of events (radioactive decays) in a fixed time interval. If each event is assumed to occur independently and spontaneously with a...


Platforms: Mac