pyhsmm
Library for Bayesian HMM and HSMM inference with nonparametric extensions.
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—Overview
What is pyhsmm?
PyHSMm is a Python library that provides tools for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs). It focuses on the Bayesian Nonparametric extensions, HDP-HMM and HDP-HSMM, with weak-limit approximations.
Key differentiator
“PyHSMm stands out by providing specialized tools for Bayesian nonparametric models with HMMs and HSMMs, focusing on weak-limit approximations which are not commonly found in other libraries.”
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Who is it for?
✓ Best for
Researchers working with Bayesian nonparametric models who need efficient inference methods for HMMs and HSMMs.
Academic projects focusing on unsupervised time-series analysis using Bayesian approaches.
✕ Not a fit for
Projects requiring real-time or near-real-time processing of large datasets, as the library is designed for research and may not be optimized for speed.
Applications that require a graphical user interface (GUI) for model specification and inference.
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Get Started with pyhsmm
Step-by-step setup guide with code examples and common gotchas.