https://github.com/bhmm/bhmm
Bayesian hidden Markov models toolkit
https://github.com/bhmm/bhmm
baum-welch-algorithm bayesian hidden-markov-models hmm hmm-viterbi-algorithm
Last synced: 4 months ago
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Bayesian hidden Markov models toolkit
- Host: GitHub
- URL: https://github.com/bhmm/bhmm
- Owner: bhmm
- License: lgpl-3.0
- Created: 2015-07-09T04:20:13.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2020-08-25T07:50:53.000Z (over 5 years ago)
- Last Synced: 2025-10-21T20:48:23.569Z (4 months ago)
- Topics: baum-welch-algorithm, bayesian, hidden-markov-models, hmm, hmm-viterbi-algorithm
- Language: Python
- Size: 884 KB
- Stars: 47
- Watchers: 4
- Forks: 14
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://travis-ci.org/bhmm/bhmm)
# Bayesian hidden Markov model toolkit
This toolkit provides machinery for sampling from the Bayesian posterior of hidden Markov models with various choices of prior and output models.
## Installation
### Installation from conda
The easiest way to install `bhmm` is via the [`conda` package manager](http://conda.pydata.org/):
```
conda config --add channels conda-forge
conda install bhmm
```
### Installation from source
```
python setup.py install
```
## References
See [here](http://arxiv.org/abs/1108.1430) for a manuscript describing the theory behind using Gibbs sampling to sample from Bayesian hidden Markov model posteriors.
> Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty.
> John D. Chodera, Phillip Elms, Frank Noé, Bettina Keller, Christian M. Kaiser, Aaron Ewall-Wice, Susan Marqusee, Carlos Bustamante, Nina Singhal Hinrichs
> http://arxiv.org/abs/1108.1430
## Package maintainers
* Frank Noé , Freie Universität Berlin
* Martin K. Scherer , Freie Universität Berlin
* John D. Chodera , Sloan Kettering Institute