Multivariate normal probability density function
returns an n-by-y
= mvnpdf(X
)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.