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:
Posi
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.
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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
(http://scikit-learn.org/stable/modules/generated/sklearn.mixture.Gauss
