Hello,
I am using the bnlearn package in R to handle large amounts of data in
Bayesian networks. The variables are discrete and have more than 3
million observations.
With bn.fit function I could easily get the conditional probability
distribution. However, some variables have unobserved values ??(i.e.,
NA or NaN). In some variables, unobserved values ??are almost 1
million. This is a lot to just delete them.
In tests, I've got this:
nw.fit <-bn.fit (nw, date, method = 'bayes')
Error in check.data (date): the data set contains null / NaN / NA values??.
So, how could I deal with the data and get the conditional probability
distribution?
Could someone help me?
Regards.
Alexsandro Cândido de Oliveira Silva
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