Using the bnlearn package I can learn the structure of a BN just by passing my dataset as parameter, for example:
bn1 <- blnearn :: hc (dataset)
Or must I pass some edges as prior knowledge eg:
wl = data.frame (from = c ("A", "B"), to = c ("B", "C")) bn1 <- blnearn :: hc (datase, whitelist = wl)
What I mean is the bnlearn algorithms has capacity to learn the structure from data only or always need some help with prior knowledge.
bnlearn features both structural learning and manual creation of structures in your network.
Basic structural learning is as easy as you assumed:
bn1 <- hc(x = dataset)
If you have prior knowledge about the structure that you want to include, you can use the whitelist or blacklist argument. But this is optional.
For starters I suggest this Introductory tutorial on Bayesian networks in R by Jacinto Arias including an example on structural learning.
