Hi Shankar.
> Thank you very much for offering to mentor my project! But sadly, it
> looks like sklearn is not in the GSoC organization list :(...
>
Scikit-learn is not on the organzation list but it is part of GSoC
through the Python Software Foundation..
> To answer your question about how i'll go about implementing the first
> case:
>
> The first thing to decide would be how to represent the DAG. For that,
> i could either use something like py_graph, or roll my own, like so:
>
> dag = {'A': ['B', 'C'],
> 'B': ['C', 'D']}
>
> dag represents a graph with nodes A, B, C, D and edges from A to B,C
> and from B to C,D.
>
> Once we have that, we will next need to represent factors. Factors are
> similar to conditional probability distributions, except that they
> don't normalize to unity. Inference operations on bayes nets are done
> by manipulating factors. Each factor has a scope: for example, phi(X1,
> X2, X3) has a scope of X1, X2, X3. phi(X1=x1, X2=x2, X3=x3) = y means
> that the factor phi attains value y when X1, X2, X3 are assigned the
> values x1, x2, x3.
>
> I'll design a custom class that will hold the scope of the factors,
> and have a mechanism to set/get the factor values for each assignment
> of the variables in it's scope.
>
> Finally, i'll implement methods for factor multiplication and
> marginalization. Once these are done, we can now do inference
> operations on our DAG.
>
That sounds like a good approach in general. The reason I was asking is
that this involves a lot of custom data structures,
that we tried to avoid in Scikit-learn.
Also your approach would be very close to what pymc does (I think), so
maybe your implementation would
better fit in their framework.
This is just my personal impression, though, and I don't know what the
others think.
Cheers,
Andy
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