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Yet Another Bayesian Framework
Why another Bayesian Framework? There are a relative host_ of Bayesian codes in Python, including major players such as PyMC3_, emcee_ and stan_, as well as a seemingly never-ending set of scientific-field-specific codes (eg. cosmology has CosmoMC_, CosmoHammer_, MontePython_, cobaya_...).
yabf was written because the author found that all the frameowrks they tried
were either too lean or too involved. yabf tries to find the happy medium.
It won't be the right tool for everyone, but it might be the right tool for you.
yabf is designed to support "black box" likelihoods, by which we mean those
that don't necessarily have analytic derivatives. This separates it from codes
such as PyMC3_ and stan_, and limits its use to samplers that do not require
that information. This is more often the case in scientific applications, where
likelihoods can in principle depend on some enormous black-box simulation code.
Thus, in this regard it is more like emcee_ or polychord_.
On the other hand, yabf is not another MCMC sampler. Apart from the
limitations concerning likelihood derivatives, it is sampler-agnostic. It is
rather a specification of a format, and an implementation of that specification.
That is, it specifies that likelihoods should have certain properties (like
parameters), and gives tools that enable that. Or as another example, it
specifies that samplers should contain certain attributes pre- and post-sampling.
In this regard, yabf is more like PyMC3_ or stan_, and unlike emcee_ or
polychord_.
yabf is perhaps most similar to codes such as CosmoHammer_ or cobaya_,
which provide an interface for creating (cosmological) likelihoods which can
then be sampled by somie specified sampler. However, yabf is different in
that it is intended to be field-agnostic, and entirely general. In addition,
I found that these codes didn't quite satisfy my criteria for ease-of-use
and extensibility.
I hope that yabf provides these. Here are a few of its features:
Component or Likelihood classes, and it is immediately useable.This package was created with Cookiecutter_ and the
audreyr/cookiecutter-pypackage_ project template.
Many of the ideas in this code are adaptations of other MCMC codes, especially CosmoHammer_ and cobaya_.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage
.. _host: https://github.com/Gabriel-p/pythonMCMC
.. _PyMC3: https://docs.pymc.io/
.. _emcee: https://emcee.readthedocs.io/en/latest/tutorials/quickstart/
.. _stan: https://pystan.readthedocs.io/en/latest/
.. _CosmoMC: https://cosmologist.info/cosmomc/
.. _CosmoHammer: https://github.com/cosmo-ethz/CosmoHammer
.. _MontePython: http://baudren.github.io/montepython.html
.. _cobaya: https://cobaya.readthedocs.io/en/latest/
.. _polychord: https://github.com/PolyChord/PolyChordLite
FAQs
Yet Another Bayesian Framework
We found that yabf demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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