In order to calculate the CDF of a multivariate normal, I followed this example (for the univariate case) but cannot interpret the output produced by scipy:
from scipy.stats import norm
import numpy as np
mean = np.array([1,5])
covariance = np.matrix([[1, 0.3 ],[0.3, 1]])
distribution = norm(loc=mean,scale = covariance)
print distribution.cdf(np.array([2,4]))
The output produced is:
[[ 8.41344746e-01 4.29060333e-04]
[ 9.99570940e-01 1.58655254e-01]]
If the joint CDF is defined as:
P (X1 ≤ x1, . . . ,Xn ≤ xn)
then the expected output should be a real number between 0 and 1.
After searching a lot, I think this blog entry by Noah H. Silbert describes the only readymade code from a standard library that can be used for computing the cdf for a multivariate normal in Python. Scipy has a way to do it but as mentioned in the blog, it is difficult to find. The approach is based on a paper by Alan Genz’s.
From the blog, this is how it works.
from scipy.stats import mvn
import numpy as np
low = np.array([-10, -10])
upp = np.array([.1, -.2])
mu = np.array([-.3, .17])
S = np.array([[1.2,.35],[.35,2.1]])
p,i = mvn.mvnun(low,upp,mu,S)
print p
0.2881578675080012
The scipy multivariate_normal from v1.1.0 has a cdf function built in now:
from scipy.stats import multivariate_normal as mvn
import numpy as np
mean = np.array([1,5])
covariance = np.array([[1, 0.3],[0.3, 1]])
dist = mvn(mean=mean, cov=covariance)
print("CDF:", dist.cdf(np.array([2,4])))
CDF: 0.14833820905742245
Documentation for v1.4.1 can be found here.
If you don't care about performance (i.e. perform it only occasionally), then you can create the multivariate normal pdf using multivariate_normal, and then calculate the cdf by integrate.nquad
