jmschrei/pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
This is a Python library for building and working with probabilistic models like Gaussian mixture models, Hidden Markov Models, and Bayesian networks. It takes in your raw data and can identify underlying patterns, classify sequences, or predict future states based on learned probabilities. Data scientists, machine learning engineers, and researchers who need flexible and fast probabilistic modeling will find this useful.
3,512 stars. No commits in the last 6 months.
Use this if you need to build complex probabilistic models with various distribution types, require GPU acceleration for performance, or want to integrate probabilistic models with PyTorch-based deep learning workflows.
Not ideal if you need a pre-built, out-of-the-box solution with a simple, high-level API and don't require custom probabilistic model construction.
Stars
3,512
Forks
597
Language
Python
License
MIT
Category
Last pushed
Mar 06, 2025
Commits (30d)
0
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