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PyMC3 HMM

Hidden Markov models in PyMC3.

Fully implemented PyMC3 Distribution classes for HMM state sequences (DiscreteMarkovChain) and mixtures that are driven by them (SwitchingProcess)
A forward-filtering backward-sampling (FFBS) implementation (FFBSStep) that works with NUTS—or any other PyMC3 sampler
A conjugate Dirichlet transition matrix sampler (TransMatConjugateStep)
Support for time-varying transition matrices in the FFBS sampler and all the relevant Distribution classes

To use these distributions and step methods in your PyMC3 models, simply import them from the pymc3_hmm package.

See the examples directory for demonstrations of the aforementioned features. You can also use Binder to run the examples yourself.

Installation

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 git@github.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 pre-commit hooks:

$ 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.

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for pymc3-hmm 0.2.5
File	Size	Uploaded
pymc3-hmm-0.2.5.tar.gz	37.4 kB	Apr 29, 2022	Details

Download URL	pymc3-hmm-0.2.5.tar.gz
Size	37.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`85a7ac3ff732aedf198b7ad1fa25e754f1fa83e0651c5da8e4bf51ddafa71342`
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Upload date	Apr 29, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.0 CPython/3.9.12`