Hidden Markov Models in PyMC3
Hidden Markov models in PyMC3.
- Fully implemented PyMC3
Distributionclasses for HMM state sequences (
DiscreteMarkovChain) and mixtures that are driven by them (
- A forward-filtering backward-sampling (FFBS) implementation (
FFBSStep) that works with NUTS—or any other PyMC3 sampler
- A conjugate Dirichlet transition matrix sampler (
- Support for time-varying transition matrices in the FFBS sampler and all the relevant
To use these distributions and step methods in your PyMC3 models, simply import them from the
See the examples directory for demonstrations of the aforementioned features. You can also use Binder to run the examples yourself.
Currently, the package can be installed via
pip directly from GitHub
$ pip install git+https://github.com/AmpersandTV/pymc3-hmm
First, pull in the source from GitHub:
$ git clone email@example.com:AmpersandTV/pymc3-hmm.git
Next, you can run
make conda or
make venv to set up a virtual environment.
Once your virtual environment is set up, install the project, its dependencies, and the
$ pip install -r requirements.txt $ pre-commit install --install-hooks
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.
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