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!
zhihua li
2005-Mar-25 05:13 UTC
[R] learning networks with a large number of variables and pre-set parents.
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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