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Bayesian methods for analytical relationships

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making Bayesian modelling easy(er)

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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.

An example of the type of figures that uravu can produce. Showing straight line distribution with increasing uncertainty.

In an effort to make the uravu API friendly to those new to Bayesian inference, uravu is opinionated, making assumptions about priors amoung 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:


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 build
python install 


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