FYR, here is a pretty good comparison of the existing graphical software
http://www.cs.ubc.ca/~murphyk/Software/bnsoft.html . However, I am really
concerned about the efficiency issue.
On Thu, Mar 15, 2012 at 6:03 AM, Andreas Mueller
<[email protected]>wrote:
> 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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--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
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