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Compute the Multivariate Normal pdf. Compute and plot the pdf of a multivariate normal distribution. mu = [0 0]; Sigma = [.25 .3; .3 1]; x1 = -3:.2:3; x2 = -3:.2:3; [X1,X2] = meshgrid(x1,x2); F = mvnpdf([X1(:) X2(:)],mu,Sigma); F = reshape(F,length(x2),length(x1)); surf(x1,x2,F); caxis([min(F(:))-.5*range(F(:)),max(F(:))]); axis([-3 3
This MATLAB function returns the cumulative probability of the multivariate normal distribution with zero mean and identity covariance matrix, evaluated at each row of X.
This MATLAB function returns the n-by-1 vector y, containing the probability density of the multivariate normal distribution with zero mean and identity covariance matrix, evaluated at each row of the n-by-d matrix X.
3 Dec 2011 When working in high-dimensional space, pdf values tend to be low. They can often be so low that doubles lack the precision to represent them so mvnpdf(x,mu,sigma) would break. To work around this problem, you take the log of the equation for the normal pdf, instead of just running log on the output of
Compute and plot the pdf of a multivariate normal distribution. mu = [0 0]; Sigma = [.25 .3; .3 1]; x1 = -3:.2:3; x2 = -3:.2:3; [X1,X2] = meshgrid(x1,x2); F = mvnpdf([X1(:) X2(:)],mu,Sigma); F = reshape(F,length(x2),length(x1)); surf(x1,x2,F); caxis([min(F(:))-.5*range(F(:))
Evaluate the multivariate normal distribution, generate pseudorandom samples.
Documentation. Toggle navigation. Trial Software · Product Updates; Translate This Page. Documentation Home · Statistics and Machine Learning Toolbox · Examples · Functions and Other Reference · Release Notes · PDF Documentation. Multivariate Gaussian Distribution
This MATLAB function returns an n-by-d matrix R of random vectors chosen from the multivariate normal distribution with mean MU, and covariance SIGMA.
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