Multivariate normal density in Python?

Question

Is there any python package that allows the efficient computation of the PDF (probability density function) of a multivariate normal distribution?

It doesn\'t seem to be

Answer 1

The following code helped me to solve,when given a vector what is the likelihood that vector is in a multivariate normal distribution.

import numpy as np
from scipy.stats import multivariate_normal

data with all vectors

d= np.array([[1,2,1],[2,1,3],[4,5,4],[2,2,1]])

mean of the data in vector form, which will have same length as input vector(here its 3)

mean = sum(d,axis=0)/len(d)

OR
mean=np.average(d , axis=0)
mean.shape

finding covarianve of vectors which will have shape of [input vector shape X input vector shape] here it is 3x3

cov = 0
for e in d:
  cov += np.dot((e-mean).reshape(len(e), 1), (e-mean).reshape(1, len(e)))
cov /= len(d)
cov.shape

preparing a multivariate Gaussian distribution from mean and co variance

dist = multivariate_normal(mean,cov)

finding probability distribution function.

print(dist.pdf([1,2,3]))

3.050863384798471e-05

The above value gives the likelihood.