making Bayesian modelling easy(er)
uravu (from the Tamil for relationship) is about the relationship between some data and a function that may be used to describe the data.
The aim of
uravu is to make using the amazing Bayesian inference libraries that are available in Python as easy as scipy.optimize.curve_fit.
Therefore enabling many more to make use of these exciting tools and powerful libraries.
Plus, we have some nice plotting functionalities available in the
plotting module, capable of generating publication quality figures.
In an effort to make the
uravu API friendly to those new to Bayesian inference,
uravu is opinionated, making assumptions about priors among other things.
However, we have endevoured to make it straightforward to ignore these opinions.
In addition to the library and API, we also have some basic tutorials discussing how Bayesian inference methods can be used in the analysis of data.
Bayesian inference in Python
There are a couple of fantastic Bayesian inference libraries available in Python that
uravu makes use of:
- emcee: enables the use of the Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler to evaluate the structure of the model parameter posterior distributions,
- dynesty: implements the nested sampling algorithm for evidence estimation.
If you discover any issues with
uravu please feel free to submit an issue to our issue tracker on Github.
Alternatively, if you are feeling confident, fix the bug yourself and make a pull request to the main codebase (be sure to check out our contributing guidelines first).
Finally, if you are just wanting to ask a question and cannot find the information elsewhere, we have a gitter chat room as another way to seek support.
pip install -r requirements.txt python setup.py build python setup.py install pytest