jmschrei/pomegranate
A Python library for probabilistic modeling using PyTorch, supporting mixture models, Bayesian networks, and hidden Markov models.
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Pomegranate is a modular probabilistic modeling library that treats all models as probability distributions, enabling flexible composition of components like distributions, Bayesian networks, and hidden Markov models. The recent v1.0.0 rewrite migrated from Cython to PyTorch as the computational backend, providing GPU acceleration and modern deep learning integration while maintaining the same probabilistic modeling capabilities.
Frequently asked
- What is jmschrei/pomegranate?
- A Python library for probabilistic modeling using PyTorch, supporting mixture models, Bayesian networks, and hidden Markov models.
- Is pomegranate open source?
- Yes — jmschrei/pomegranate is open source, released under the MIT license.
- What language is pomegranate written in?
- jmschrei/pomegranate is primarily written in Python.
- How popular is pomegranate?
- jmschrei/pomegranate has 3.5k stars on GitHub.
- Where can I find pomegranate?
- jmschrei/pomegranate is on GitHub at https://github.com/jmschrei/pomegranate.