mvnpdf
              Multivariate normal probability density function (PDF).
 y = mvnpdf (x) returns the probability density of the
 multivariate normal distribution with zero mean and identity covariance
 matrix, evaluated at each row of x.  Rows of the N-by-D matrix x
 correspond to observations orpoints, and columns correspond to variables or
 coordinates.  y is an N-by-1 vector.
 y = mvnpdf (x, mu) returns the density of the
 multivariate normal distribution with mean MU and identity covariance matrix,
 evaluated at each row of x.  mu is a 1-by-D vector, or an N-by-D
 matrix, in which case the density is evaluated for each row of x with
 the corresponding row of mu.  mu can also be a scalar value,
 which MVNPDF replicates to match the size of x.
 y = mvnpdf (x, mu, sigma) returns the density
 of the multivariate normal distribution with mean mu and covariance
 sigma, evaluated at each row of x.  sigma is a D-by-D
 matrix, or an D-by-D-by-N array, in which case the density is evaluated for
 each row of x with the corresponding page of sigma, i.e.,
 mvnpdf computes y(i) using x(i,:) and sigma(:,:,i).
 If the covariance matrix is diagonal, containing variances along the diagonal
 and zero covariances off the diagonal, sigma may also be specified as a
 1-by-D matrix or a 1-by-D-by-N array, containing just the diagonal. Pass in
 the empty matrix for mu to use its default value when you want to only
 specify sigma.
 If x is a 1-by-D vector, mvnpdf replicates it to match the
 leading dimension of mu or the trailing dimension of sigma.
Source Code: mvnpdf
| 
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = mvnpdf (x, mu, sigma);
 surf (X1, X2, reshape (p, 25, 25));
                     | 
