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Build Status

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:

conda config --add channels conda-forge
conda install bhmm

Installation from source

python setup.py install

References

See here 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

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