pyhsmm
Library for Bayesian HMM and HSMM inference with nonparametric extensions.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Understanding and implementing Bayesian Nonparametric extensions like HDP-HMM and HDP-HSMM requires a strong background in statistics and machine learning.
The official documentation is sparse on detailed examples and explanations of the more complex functionalities such as weak-limit approximations.
Bayesian inference can be computationally expensive, especially for nonparametric models like HDP-HMM and HDP-HSMM, leading to slow performance on large datasets.
The open-source project has a relatively small user base, which can lead to fewer contributions and slower resolution of issues or feature requests.
Fit analysis
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.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Integrations
Next step
Get Started with pyhsmm
Step-by-step setup guide with code examples and common gotchas.