On 03/14/2012 10:52 PM, Alexandre Gramfort wrote:
hi shankar,
that sounds interesting to me. Can you come up with a few references
and are you aware of existing implementations?
I think the best reference is "Bishop: Machine Learning and Pattern
Recognition".
Though it is not used for classification there so much.
Bayesian Network is another word for "directed graphical model".
This is a very wide area and I'm not sure this is easy to implement in
a generic way.
Are you thinking about discrete models? Then libDAI
<http://cs.ru.nl/%7Ejorism/libDAI/> is
the one-in-all solution that is often used afaik. Not sure if
it can learn structure, to, though.
What kind of structure would you like to learn? I would
guess you'd restrict yourself to DAG.
I'm a bit tired now so it might be I don't see the obvious but actually
I don't think I really understand the proposal.
What should be the input and what the output? Are there hidden states?
If your inputs are deterministic, I'm not sure what you would gain by
having a directed graphical model - if you don't have hidden states.
And if you do have hidden states, then I'm not quite sure what the
structure learning does...
Can you elaborate a bit more?
Thanks,
Andy
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