Documation of the bayes_mxne package¶

Installation¶

We recommend the Anaconda Python distribution. To install bayes_mxne, you first need to install its dependencies which are MNE and numba.

For instructions on how to install MNE see: http://martinos.org/mne/stable/install_mne_python.html

For numba:

$ conda install numba

should do it.

If you want to install the latest version of the code (nightly) use:

$ pip install https://api.github.com/repos/agramfort/bayes_mxne/zipball/master

If you do not have admin privileges on the computer, use the --user flag with pip. To upgrade, use the --upgrade flag provided by pip.

To check if everything worked fine, you can do:

$ python -c 'import bayes_mxne'

and it should not give any error messages.

Bug reports¶

Use the github issue tracker to report bugs.

Cite¶

[1] Bekhti, Y., Lucka, F., Salmon, J., & Gramfort, A. (2018). “A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging.” Inverse Problems, Volume 34, Number 8.