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[ascl:2012.016] Pomegranate: Probabilistic model builder

Pomegranate builds probabilistic models in Python that is implemented in Cython for speed. The code merges the easy-to-use API of scikit-learn with the modularity of probabilistic modeling, including general mixture and hidden Markov models and Bayesian networks, to allow users to specify complicated models without the need to be concerned about implementation details. The models are built from the ground up and natively support features such as multi-threaded parallelism and out-of-core processing.

Code site:
https://github.com/jmschrei/pomegranate
Used in:
https://ui.adsabs.harvard.edu/abs/2020arXiv200811724S
Described in:
https://ui.adsabs.harvard.edu/abs/2017arXiv171100137S https://jmlr.org/papers/v18/17-636.html
Bibcode:
2020ascl.soft12016S
Preferred citation method:

https://jmlr.org/papers/v18/17-636.html


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ascl:2012.016
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