I could not find a Python function to evaluate the multivariate normal distribution in Python. Here's one that gives equivalent results to the dmvnorm function in the mvtnorm package for R. It's something that works. I've not had time or need yet to fix it up.
b: A vector
mean: The mean of the elements in b (same dimensions as b)
cov: The covariance matrix of the multivariate normal distribution
License: GPL version 2
http://www.gnu.org/licenses/gpl-2.0.html
k = b.shape[0]
part1 = numpy.exp(-0.5*k*numpy.log(2*numpy.pi))
part2 = numpy.power(numpy.linalg.det(cov),-0.5)
dev = b-mean
part3 = numpy.exp(-0.5*numpy.dot(numpy.dot(dev.transpose(),numpy.linalg.inv(cov)),dev))
dmvnorm = part1*part2*part3
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9 comments:
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This has actually been very helpful, as I also haven't been able to find a proper implementation of the PDF of multivariate normal distributions. I was just wondering if there was any particular reason for you using
exp(-0.5*log(2*pi))
instead of
(2*pi)**-0.5.
(which gives the same result)
Of course, I meant
(2*pi)**-k/2.
@mrhoward: I'm not sure to be honest. It's been a while since I wrote this post. My recollection is that I found an implementation in R and translated it to Python.
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