# PyHHMM: A Python Library for Heterogeneous Hidden Markov Models

arXiv: https://arxiv.org/abs/2201.06968 · HTML: https://mlsanity.com/paper/2201.06968/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data](https://mlsanity.com/paper/2211.10371/) (2022)
- [Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data](https://mlsanity.com/paper/2301.10156/) (2023)
- [A Vector-Quantized Foundation Model for Patient Behavior Monitoring](https://mlsanity.com/paper/2503.15221/) (2025)

## Abstract (arXiv)

We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as different initialization algorithms and classical observations models, i.e., continuous and multinoulli, PyHHMM distinctively emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. These characteristics result in a feature-rich implementation for researchers working with sequential data. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License. PyHHMM's source code is publicly available on Github (https://github.com/fmorenopino/HeterogeneousHMM) to facilitate adoptions and future contributions. A detailed documentation (https://pyhhmm.readthedocs.io/en/latest), which covers examples of use and models' theoretical explanation, is available. The package can be installed through the Python Package Index (PyPI), via 'pip install pyhhmm'.