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

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

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