[R] learning networks with a large number of variables and pre-set parents.

[zhihua li]

I have 100 cases. So i think the dimension is (100, 10000). 
The PC has a pentium 4 CPU with 512M memory. I don't know if it is enough?



>From: Christian Schulz <ozric at web.de>
>To: zhihua li <lzhtom at hotmail.com>
>CC: r-help at stat.math.ethz.ch
>Subject: Re: [R] learning networks with a large number of variables and 
pre-set parents.
>Date: Sat, 26 Mar 2005 08:13:34 +0100
>
>Hi,
>
>you have 10000 variables and how many cases?
>In my experience you need a lot of memory working with this kind/size of
>data and deal!
>
> >> dim(pk.df)
>[1] 7321 24
> >> pk <- network(pk.df)
> >> pk.prior <- jointprior(pk)
>Error in rep.default(data, length.out = vl) :
>cannot allocate vector of length 577368000
>
>Perhaps this is usefuel for you?
>
>Ines - Induction of Network Structure
>(learning probabilistic and possibilistic graphical models)
>
>http://fuzzy.cs.uni-magdeburg.de/~borgelt/ines.html
>
>regards,
>Christian
>
>
>zhihua li schrieb:
>
> > hi netters:
> > I have a series of discrete variables which form a network and I want
> > to learn the network structure from some training data. I could have
> > used packages like deal but there are two problems.
> > First of all, I have 10000 variables. So the possible network
> > structure is awfully huge, I don't know how long it will take my PC to
> > find the highest-scoring network..........maybe a month? Secondly, I
> > have some prior knowledge that only 500 out of the 10000 variales are
> > possible parents. In another word, only those arrows startting from
> > the 500 variables and pointing to the remaining 99500 variables are
> > allowed in the network. In deal an assignment to "banlist" should help
> > me rule out the impossible arrows. But in my case the number of
> > "impossible arrows" is 500*499+99500*99549, and so the "banlist" would
> > get unacceptable long. Are there any methods (in deal or other
> > packages) to specify the parents set in advance?
> > Thanks a lot!
> >
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>