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pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution, Bayesian networks can be dropped into a mixture just as easily as a normal distribution, and hidden Markov models can be dropped into Bayes classifiers to make a classifier over sequences. Together, these two design choices enable a flexibility not seen in any other probabilistic modeling package.
Features
- Native PyTorch is usually significantly faster
- Documentation available
- Serialization, mixed precision, and GPU support, that can now be directly used
- All models now have GPU support
- All models now have support for half/mixed precision
- All distributions are now multivariate by default and treat each feature independently (except Normal)
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