**********
Conclusion
**********

MCMC is a surprisingly difficult and bug-prone algorithm to implement by hand. 
We find PyMC makes it much easier and less stressful. PyMC also makes our work 
more dynamic; getting hand-coded MCMC's working used to be so much work that we 
were reluctant to change anything, but with PyMC changing models is much easier.

We welcome new contributors at all levels. If you would like to contribute new 
code, improve documentation, share your results or provide ideas for new 
features, please introduce yourself on our `mailing list`_. Our `wiki page`_. 
also hosts a number of tutorials and examples from users that could give you 
some ideas. We have taken great care to make the code easy to extend, whether 
by adding new database backends, step methods or entirely new sampling 
algorithms.

.. _`mailing list`: pymc@googlegroups.com

.. _`wiki page`: https://github.com/pymc-devs/pymc/wiki


****************
Acknowledgements
****************

The authors would like to thank several users of PyMC who have been 
particularly helpful during the development of the 2.0 release. In alphabetical 
order, these are Mike Conroy, Abraham Flaxman, J. Miguel Marin, Aaron MacNeil, 
Nick Matsakis, John Salvatier, Andrew Straw and Thomas Wiecki.

Anand Patil’s work on PyMC has been supported since 2008 by the Malaria Atlas 
Project, principally funded by the Wellcome Trust.

David Huard’s early work on PyMC was supported by a scholarship from the 
Natural Sciences and Engineering Research Council of Canada.
