Multivariate normal probability density function

returns an `y`

= mvnpdf(`X`

)*n*-by-`1`

vector
`y`

containing the probability density function (pdf) values
for the *d*-dimensional multivariate normal distribution with zero
mean and identity covariance matrix, evaluated at each row of the
*n*-by-*d* matrix `X`

. For
more information, see Multivariate Normal Distribution.

In the one-dimensional case,

`Sigma`

is the variance, not the standard deviation. For example,`mvnpdf(1,0,4)`

is the same as`normpdf(1,0,2)`

, where`4`

is the variance and`2`

is the standard deviation.

[1] Kotz, S., N. Balakrishnan, and
N. L. Johnson. *Continuous Multivariate Distributions: Volume 1: Models and
Applications.* 2nd ed. New York: John Wiley & Sons, Inc.,
2000.