I see, thanks for clearning that up. I was aware of the fact for uniform
distributions, but not for normal ones.
So that would mean, some of the components have such a small variance
that the loglik is positive in the end?
Cheers,
Simon
Am 30.05.18 um 11:22 schrieb robin.e...@xense.co.uk:
Positive log likelihoods for continuous distributions are not unusual. You are
evaluating a pdf not a probability. For example a univariate Gaussian pdf
returns greater than 1 at the mean when the variance goes below 0.39, at which
point the log pdf is positive.
Sent from Polymail (
Hey,
sorry for the late reply. I cannot share the data but the problem can be
reproduced easily, like below.
I wanted to check with sklearn and observe a similar behaviour, i.e. a
positive per-sample average log-likelihood
Dear all,
I am fitting a very trivial GMM with 2-10 components on 100 samples and
5 features in pyspark and observe some of the log-likelihoods being
positive (see below). I don't undestand how this is possible. Is this a
bug or an intended behaviour? Furthermore, for different seeds,
