[Neuroimaging] Interest in modeling library for NiPy

Marmaduke Woodman mmwoodman at gmail.com
Mon Nov 7 05:11:45 EST 2016

On Fri, Nov 4, 2016 at 7:18 PM, John Griffiths <j.davidgriffiths at gmail.com>

the distinction is between inference on parameters vs. inference on models
> (parametric/non-parametric has separate meanings); and not DCM's estimates
> of effective connectivity parameters per se but rather model
> evidence/fit/frenergy metrics and comparisons thereof. Certainly it is
> essential to support both.

I would focus first on the former: an API would allow specification of a
dataset, a generative model and an inference scheme; the results would be
inference diagnostics and posteriors.

One could build on that to specific multiple models or a model space and
comparison criteria.

Anyone with experience in DCM's API might be able to suggest how to make
that user friendly?

> PyMC3 does look like the way to go.

Edward (http://edwardlib.org) is a new one also worth looking at, because
it builds mainly on TensorFlow. I'm not sure even HMC will scale to full
size neuroimaging data (though networks with several or tens of nodes would
work), so it's important to keep the variational schemes available.
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