Hello all!
I've just released a new version of pomegranate, which is a probabilistic
modelling package for Python with a speedy cython implementation. It
currently supports the following:
* a wide range of probability distributions
* general mixture models
* hidden markov models
* naive bayes
* markov chains
* discrete bayesian networks
* factor graphs
* finite state machines
It currently outperforms other population implementations in terms of
training time, such as hmmlearn for hmms and scikit-learn for GMM and Naive
Bayes.
Please see my full post here:
https://www.reddit.com/r/Python/comments/4cllym/pomegranate_v040_fast_and_flexible_probabilistic/
and let me know if you have any questions of comments! I'd love any
feedback you have.
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