/pymc3-hmm

Hidden Markov models in PyMC3

Primary LanguagePythonOtherNOASSERTION

Build Status

PyMC3 HMM

Hidden Markov models in PyMC3.

Features

  • Fully implemented PyMC3 Distribution classes for HMM state sequences and mixtures that depend on them
  • A forward-filtering backward-sampling (FFBS) implementation that works with NUTS—or any other PyMC3 sampler
  • A conjugate Dirichlet transition matrix sampler
  • Support for time-varying transition matrices in both the Distribution classes and FFBS sampler

Installation

The package name is pymc3_hmm and it can be installed with pip directly from GitHub

$ pip install git+https://github.com/nccmedia/pymc3-hmm

Development

First, pull in the source from GitHub:

$ git clone git@github.com:NCCMedia/pymc3-hmm.git

Afterward, you can run make conda or make venv to set up a virtual environment. After making changes, be sure to run make black in order to automatically format the code and then make check to run the linters and tests.

License

Apache License, Version 2.0